Patent Publication Number: US-2021183524-A1

Title: Method and system for providing interpretation  information on pathomics data

Description:
CROSS-REFERENCES TO THE RELATED APPLICATIONS 
     This application claims priority from Korean Patent Application No. 10-2019-0168111 filed on Dec. 16, 2019, in the Korean Intellectual Property Office, the disclosure of which is incorporated herein by reference in its entirety. 
     BACKGROUND 
     (a) Field 
     The present disclosure relates to digital pathology. 
     (b) Description of the Related Art 
     Researches to figure out whether a patient suffers a disease or to determine a status of the disease are have been performed through various molecular markers such as an mRNA, a protein, and the like. Recently, in order to find a biomarker that enables to figure out the disease status more accurately and consistently, researches for finding a molecular marker showing a specific pattern have been performed by using various omics data for each disease status. 
     Meanwhile, pathology is the study of organic and functional changes in the tissues and organs of the body where inflicted by a disease. In methodological aspect, pathology is rapidly shifting from traditional pathology where tissues or cells taken from a human body are placed on a glass slide and observed with an optical microscope, to digital pathology. 
     Digital pathology refers to a system that converts the glass slide into a digital image, and analyzes, stores, and manages the digital images. As a method for converting the glass slide into a digital image, a whole slide imaging (WSI) method may be used, in which part or all of the contents of the glass slide is scanned with high magnification and then digitized. 
     A slide image obtained through WSI provides a large amount of visual information that can be seen at the cell level, and thus may be used as important data for diagnostic medicine. A recently developed AI pathology analyzer such as Lunit SCOPE enables comprehensive analysis of tissue cells and further enables a large amount of data not having been utilized so far to be made in a feasible form. In particular, the Lunit SCOPE may generate data called “pathomics” from the slide image, through cell classification, tissue classification, and structure classification. The term “pathomics” refers to histopathological data containing information of all histologic components obtained from a pathology slide image. Features extracted from the slide image through histopathologic analysis may be used as a biomarker for prognostic prediction, reactivity prediction of anticancer drugs, and clinical decision. 
     On the other hand, although the pathomics data contains a lot of information, biological and/or medical explanation and interpretation of the histological data should comes first in order to clinically utilize such information. However, histopathology techniques up to now does not biologically and/or medically interpret the extracted result (histopathology data) from the slide image, and not provide the biological and medical meaning thereof. Thus, it is difficult for a user to understand the features extracted from the AI slide image analyzer. Additionally, due to the absence of biological and medical information of the features extracted from the slide image, there is a limit that the means for evaluating the reliability of the AI pathology analyzer is not provided. 
     SUMMARY 
     The present disclosure provides a method and a system for providing biological and/or medical interpretation information of pathomics data extracted from a slide image. 
     The present disclosure provides a method and a system for analyzing relationship between pathomics data and modularized genetic information, and providing biological and/or medical interpretation information of pathomics data by using a function of a gene module related to the pathomics data. 
     The present disclosure provides a method and a system for visualizing biological and/or medical interpretation information of pathomics data. 
     According to an embodiment of the present disclosure, an operation method of a computing device operated by at least one processor may be provided. The operation method comprises receiving pathomics data samples analyzed from slide images of patients and gene samples of the patients, generating a plurality of gene modules by grouping genetic information included in the gene samples, annotating information of databases significantly enriched in each of the gene modules, to a corresponding gene module, based on one-to-one correlation values between the plurality of the gene modules and a plurality of individual pathomics data representing the pathomics data samples, extracting connectivity between the plurality of the individual pathomics data and the plurality of gene modules, and connecting information annotated to each gene module and the individual pathomics data connected to the corresponding gene module. 
     Generating the plurality of gene modules may comprises, based on correlations among RNAs and/or proteins included in the gene samples, modularizing the RNAs and/or proteins into the plurality of gene modules. 
     Each of the gene samples may include quantitative data that are obtained through measuring the RNAs and/or proteins by transcriptome analysis and/or proteome analysis. 
     The databases may be selected from databases that provide relationship information between biologically discovered genes and functions, gene feature information including pathways and interaction information, and medicine and pharmacy information. 
     Annotating information of databases may comprise determining information of the databases significantly enriched in each of the gene modules through enrichment analysis. 
     Extracting the connectivity may comprise shortening a value of each of the gene modules in a designated method and determining existence of a relationship between each of the gene modules and each individual pathomics data by using the shortened value of each of the gene modules. 
     The operation method may further comprises providing information annotated to each of the gene modules as interpretation information of individual pathomics data connected to corresponding gene module. 
     The individual pathomics data may be a parameter representing cellular information and structural information of a pathological image, and a value of the individual pathomics data may be determined by a representative value of the quantitative data of corresponding parameter in the pathomics data samples. 
     According to an embodiment, a computing device may be provided. The computing device may comprise a memory and at least one processor that executes instructions of a program loaded in the memory. The processor may generates a plurality of gene modules by grouping genetic information of patients, determine a gene module correlated with pathomics data among the plurality of gene modules, and connect information of databases significantly enriched in each of the gene modules to the pathomics data correlated with corresponding gene module. The pathomics data may be composed of parameters representing cellular information and structural information of pathological images and each parameter may be represented as quantitative data. The pathological images may be obtained from the patients who provide the genetic information. 
     The processor may modularize RNAs and/or proteins into the plurality of gene modules, based on correlations among the RNAs and/or the proteins included in the genetic information. 
     The processor may determine information of the databases significantly enriched in each genetic module through enrichment analysis. 
     The processor may shorten a value of each of the gene modules in a designated method, calculate a correlation value between each of the gene module and individual pathomics data included in the pathomics data by using the shortened value of each gene module, and make a relationship between the individual pathomics data and a gene module whose correlation value is equal to or greater than a threshold. 
     The processor may annotate information of databases significantly enriched in each of the gene modules to a corresponding gene module, and provide the information annotated to each of the gene modules as interpretation information of pathomics data connected to corresponding gene module. 
     According to an embodiment, a program stored on a non-transitory computer-readable storage medium may be provided. The program may comprise instructions for causing a computing device to execute generating a plurality of gene modules by grouping genetic information of patients, annotating information of databases significantly enriched in each gene module to a corresponding gene module, determining a gene module correlated with pathomis data based on correlation values between the pathomics data and the plurality of genetic modules, and storing connectivity between the plurality of the gene modules and the pathomics data extracted based on the correlation values, and the information annotated to each of the gene modules. The pathomics data may be composed of parameters representing cellular information and structural information of pathological images, and each of the parameters may be represented as quantitative data. The pathological images may be information obtained from the patients who provide the genetic information. 
     Annotating the information of databases may comprise determining information of the databases significantly enriched in each of the gene modules through enrichment analysis, and annotating the information of the databases significantly enriched in each of the gene modules to a corresponding gene module. 
     The program may further comprises instructions for causing a computing device to execute providing the information annotated to each of the gene modules as interpretation information of the pathomics data based on a connectivity between the pathomics data and the plurality of gene modules. 
     According to some embodiments, by providing interpretation information on pathomics data extracted from slide images, biological meaning and medical meaning of the pathomics data may be interpreted and inferred. 
     According to some embodiments, the utilization of pathomics data applicable to biological and/or medical interpretation may be improved, and interpretation of features extracted from slide images may contribute to discovery of a biomarker for prognostic prediction, reactivity prediction of anticancer drugs, and clinical decision. 
     According to some embodiments, a proof for reliability of performance of an AI pathology analyzer may be afforded by providing pathomics data and biological and/or medical information connected thereto. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagram for explaining an AI pathology analyzer according to an embodiment. 
         FIG. 2  is a block diagram illustrating a system for providing interpretation information of pathomics data according to an embodiment. 
         FIG. 3  is an example of a relationship analysis result for connecting pathomics data and a gene module according to an embodiment. 
         FIG. 4  is a diagram visually representing a connection relationship between pathomics data and a gene module according to an embodiment. 
         FIG. 5  and  FIG. 6  are examples of enrichment analysis results for a gene module coded with a color name of black. 
         FIG. 7  and  FIG. 8  are example diagrams showing enrichment analysis results for a gene module coded with a color name of yellow. 
         FIG. 9  is an example interface screen on which interpretation information is visually displayed, according to an embodiment. 
         FIG. 10  is a flowchart showing a method for providing interpretation information of pathomics data according to an embodiment. 
         FIG. 11  is a hardware configuration diagram of a computing device according to an embodiment. 
     
    
    
     DETAILED DESCRIPTION OF THE EMBODIMENTS 
     Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings so that the person of ordinary skill in the art may easily implement the present disclosure. The present disclosure may be modified in various ways and is not limited thereto. In the drawings, elements irrelevant to the description of the present disclosure are omitted for clarity of explanation, and like reference numerals designate like elements throughout the specification. 
     Throughout the specification, when a part is referred to “include” a certain element, it means that it may further include other elements rather than exclude other elements, unless specifically indicates otherwise. In addition, the term such as “ . . . unit”, “ . . . block”, “ . . . module”, or the like described in the specification mean a unit that processes at least one function or operation, which may be implemented with a hardware, a software or a combination thereof. 
     Until now, most researches for interpreting pathomics data (mostly, the number of cells) are performed mainly by inferring the meaning of pathomics data through correlation analysis with a single gene. Here, in order to define the correlation, a variety of arbitrary conditions are used. However, the correlation analysis between pathomics data and genes has problems as follows. First, it is difficult to set a threshold that can define related genes among about 20,000 genes. Second, it is so difficult to find biological meaning of variables that are generated according to each tissue type and/or cell type included in the histopathology data, and thus interpretation of cells in any tissue type and/or cell type is not possible. Third, it is difficult to relate the pathomics data with previously known clinical knowledge such as disease mechanisms, drug response and the like. 
     Hereinafter, a method of relating various histological data with genetic information, and annotating biological and/or medical interpretation information to the various histological data thereby is described. First, a description of some databases that may be used to annotate biological and/or medical interpretation information will be followed. 
     Biological process terms of gene ontology may be used. The biological process refers to a process genetically programmed to make an organism accomplish specific biological purpose. The biological process is a whole process generating two daughter cells from a single mother cell through, for example, cell division. 
     Molecular function terms of gene ontology may be used. The molecular functional terms describe functions corresponding to all processes regulating catalysis, binding, biological activity, rate, and the like that occur at the molecular level. 
     KEGG pathway is a database of route maps explaining knowledge of interactions among molecules, reactions, and relation network of molecules. The KEGG pathway provides representative seven biological/medical mechanisms in the form of pathway map. The KEGG pathway contains details of metabolism, genetic information processing, environmental information processing, cellular processes, organismal systems, human diseases, and drug development, and includes pathway maps of molecular networks for each subset under each category. 
     BIOCARTA is a database about relationships such as molecular interactions, reactions, and the like. Like the KEGG pathway, the BIOCARTA introduces specific mechanisms through molecular relationships. 
     The genetic association database (GAD) is a relational database of disease and genome. The GAD is a database of open genetic association studies, which contains biological/medical information about diseases, genomes, genes, and mutations for the purpose of human-genetic association studies. Therefore, the database may be modified as describing relationships between diseases and genes by shortening information in the unit of gene, and finally may perform functional enrichment analysis along with a module that is a result of the present disclosure. 
     Online Mendelian inheritance in man (OMIM) is a database of human genes and genetic disorders. OMIM is a database containing information about all genetic disorders, such as Mendelian disease, and may define the relationship between diseases and histologic components through correlations between diseases and modules and correlations between module and histologic components. 
     UniProt Keywords is a database of keywords related to proteins. UniProt Keywords has 10 sub-categories in the keywords that are constructed as a database for proteins. The 10 sub-categories are classified as biological process, cellular component, coding sequence diversity, developmental stage, disease, domain, ligand, molecular function, post-translational modification, and technical term. Each protein is a product of a gene, and many proteins may be shortened as specific genes. Namely, the UnitProt keyword can be substituted for a keyword describing a specific gene, which enables a functional enrichment analysis with the module. 
     UniProt tissue specificity is a database providing information on gene expression at mRNA level or at protein level in a cell or a tissue of a multicellular organism. UniProt tissue specificity is a database containing information on a specific tissue where gene is expressed. From Uniprot tissue specificity, information on tissues where each module is specifically expressed may be obtained. 
       FIG. 1  is a diagram for explaining an AI pathology analyzer according to an embodiment. 
     Referring to  FIG. 1 , the AI pathology analyzer  10  is a computing device trained to receive a slide image  1  obtained through scanning diagnostic target tissue with whole slide imaging (WSI) technique, and to extract a variety of pathomics data  2  from the slide image  1 . Here, the slide image  1  represents a cross section of tissue obtained from primary tumor of a patient through biopsy or surgery, and may be referred to as a pathological image. The pathomics data  2  includes information obtained through cell classification, tissue classification, and structure classification of the slide image  1  in the AI pathology analyzer  10 . 
     The slide image  1  is produced to satisfy input conditions of the AI pathology analyzer  10 . The slide image is obtained by converting a glass slide to a digital image through whole slide imaging. In order to obtain glass slides, various biopsy methods slides may be used. For example, needle biopsy, surgical biopsy, aspiration biopsy, skin biopsy, prostate biopsy, kidney biopsy, liver biopsy, bone marrow biopsy, bone biopsy, CT-guided biopsy, ultrasound-guided biopsy, and the like may be used, but the biopsy methods are not limited thereto. 
     The AI pathology analyzer  10  may be trained with various types of slide images, and may output AI analysis data for various cancer types and quantitative data obtained by digitizing extracted features as the number, the total amount, and the like, as the pathomics data. For example, the pthomics data may be digitized as the number of lymphoplasma cells located in cancer epithelial and cancer stroma, the total amount of cancer epithelial and cancer stroma, and the like. 
     Specifically, the pthomics data may include features on area information in the slide image, such as cancer epithelial, cancer stroma, normal epithelial, normal stroma, necrosis, fat, background and the like. The phthomics data may include cell classification data obtained by structurally and/or systematically classifying cells in the slide image, and digitized quantitative data. The types of cells may be variously classified, such as a degenerated tumor cell, a necrotic tumor cell, an endothelial cell, a pericyte, a mitosis, a macrophage, a lymphoplasma cell, a fibroblast, and the like. The pathomics data may include features of a specific type of cancer. For example, the features may include features indicating anomaly of breast cancer cells, such as nuclear grade 1, nuclear grade 2, nuclear grade 3, tubule formation count, tubule formation area, ductal carcinoma in situ (DCIS) count, DCIS area, and the like. Further, the pathomics data may include nerve count, nerve area, blood vessel count, blood vessel area, and the like. 
     The AI pathology analyzer  10  may be implemented through a machine learning model that can extract meaningful features from an image. The AI pathology analyzer  10  may include separately trained models according to a diagnosis type (e.g., cancer type). For example, the AI pathology analyzer  10  may be implemented with a deep learning-based training model such as a convolutional neural network, a graph neural network, and the like. Alternatively, the AI pathology analyzer  10  may be implemented with a relatively simple classification model such as a support vector machine (SVM), a random forest, a regression model, and the like. Needless to say, the AI pathology analyzer  10  may be implemented as a combination of various machine learning models. 
       FIG. 2  is a block diagram illustrating a system for providing interpretation information of pathomics data according to an embodiment. 
     Referring to  FIG. 2 , a system for providing interpretation information of pathomics data (hereinafter, referred to as an “interpretation information providing system”)  100  may provide biological and/or medical interpretation information of pathomics data extracted from a slide image. The interpretation information providing system  100  may include the AI pathology analyzer  10  shown in  FIG. 1 , but, in the following description, pathomics data output from the AI pathological analyzer  10  is described as to be input to the interpretation information providing system  100 . The interpretation information providing system  100  may operate independently from the AI pathology analyzer  10  and may provide interpretation information about an external AI pathology analyzer by interworking with various types of external AI pathology analyzers. 
     The interpretation information providing system  100  includes phtomics data manager  110 , genetic information manager  120 , gene module generator  130 , connector between pathomics data and gene module (hereinafter, referred to as a “connector”)  150 , and an interpretation information generator  170 . For explanation, each component of the interpretation information providing system  100  is referred to as the pathomics data manager  110 , the genetic information manager  120 , the gene module generator  130 , the connector  150 , and the interpretation information generator  170 , respectively, but may be implemented as a computing device executed by at least one processor. Here, the components may be implemented in a computing device all together or implemented as distributed in separate computing devices. When implemented in separate computing devices, each component may communicate with each other via a communication interface. A device that can execute a software program designed to perform the embodiments of the present disclosure will suffice the computing device. 
     The interpretation information providing system  100  interworks with various databases  200  required by the gene module generator  130 , the connector  150 , and the interpretation information generator  170 . The various databases  200  includes a knowledge database and a literature database. The various databases may include a biological database containing genetic feature information such as relationship information between biologically discovered genes and functions, pathways, interactions, and the like, and a medical database used in medical fields such as biochemistry, medicine, pharmacy, and the like. 
     Biological databases providing genetic feature information may include, for example, a protein-protein interaction (PPI) network, a gene co-expression network, a gene regulatory network, a metabolic network, a system biology database, a protein-protein interaction database, a gene ontology database, a gene-gene interaction database, a synthetic biology database, a genetic interaction database, a gene set enrichment analysis (GSEA), a KEGG Pathway, BIOCARTA, UniProt Keywords, UniProt Tissue specificity, and the like. 
     The medical database may be a database utilized in biomedical field and may be, for example, a chemical interaction database, a disease-gene database, a gene-drug database, a gene-phenotype database, a pharmaco-genomics database, a gene-pharmacokinetic database, a gene-pharmacodynamics database, a drug-drug database, a biological pathway database, UniProt protein database, a protein domain, a protein interaction, a tissue expression, genetic association database (GAD), Online Mendelian inheritance in man (OMIM), and the like. The medical database may include a knowledge database and literature that can cluster genes and proteins. 
     In addition, the database may be Uniprot Sequence Feature (UP_SEQ_FEATURE), NCBI&#39;s COG database (COG_ONTOLOGY), PUBMED Literature ID, REACTOME pathways, biological biochemical image database (BBID), EMBL-EBI InterPro, EMBL-EBI IntAct, simple modular architecture research tool (SMART), protein information resource (PIR), BIOGRID database, and the like. 
     The interpretation information providing system  100  receives analysis data where pathomics data  2  of a patient is paired with genetic information  3 . The pathomics data  2  is raw data that is input to the phatomics data manager  110 . The genetic information  3  is raw data that is input to the genetic information manager  120 . 
     The pathomics data  2  is data output from the AI pathology analyzer  10  that receives the slide image  1  of the patient, as shown in  FIG. 1 . As such, the interpretation information providing system  100  receives samples of a plurality of patients, and the pathomics data samples and the genetic information samples are paired. It is assumed that the interpretation information providing system  100  receives pathomics data and genetic information of a patients cohort. The patients cohort refers to a group of patients diagnosed with a specific disease, and pathomics data and genetic information of patients of the same disease are used. 
     Genetic information  3  is biological information quantified such as transcriptome, proteome, and the like. For example, the genetic information  3  may include RNA information and/or protein information, which are product of gene expression. In the present disclosure, the terms RNA and protein may be used without distinction. Gene information  3  may include quantitative data of RNA and/or protein. The genetic information manager  120  may generate or modify genetic information according to the input condition of the gene module generator  130 . Genetic information  3  may be generated as a gene/protein set having a specific function by the gene module generator  130 . 
     Quantitative data of RNA may be numerically measured data of the amount of genes expressed to mRNA state. RNA quantitative data may be obtained by a transcriptomics technique that measures gene-expressed RNA. As a transcriptomics technique, for example, apolymerase chain reaction (PCR), real-time PCR (qPCR), microarray, NGS RNA sequencing, targeted RNA seqeuencing, and the like may be used. 
     Protein quantitative data is numerically measured data of expression of a protein having a function. The protein quantitative data may be obtained by a proteomics technique. As a proteomics technique, for example, reverse phase protein array (RPPA), mass spectrometry, blotting techniques for protein quantification, and the like may be used. 
     The pathomics data  2  includes data numerically quantified information of a tissue and a cell contained in the slide image. That is, the pathomics data  2  is a quantified value as the number of cells or pixels that are counted in cells, tissues, and structures. 
     The pathomics data output from a Lunit SCOPE may be coded, for example, as shown in Table 1. In table 1, CE and CS may refer to cancer epithelial and cancer stroma, respectively. Each code may be abbreviation of the names of the tissue/cell. For example, CE stands for cancer epithelium, CS stands for cancer stroma, NE stands for normal epithelium, NS stands for normal stroma, N stands for necrosis, F stands for fat, PC stands for endothelial cell and pericyte, MTS stands for mitosis, MA stands for macrophage, TIL stands for lymphoplasma cell, FB stands for fibroblast, N1 stands for Nuclear grade 1, N2 stands for Nuclear grade 2, N3 stands for Nuclear grade 3, TB stands for tubule formation, DCIS stands for ductal carcinoma in situ (DCIS), NV stands for nerve, and BV stands for blood vessel. PER and DEN stands for percentage and density, respectively. Each code can be used for interpret the meaning of the data. 
     
       
         
           
               
               
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 No. 
                 Pathomics 
                 Description 
               
               
                   
               
             
            
               
                 P1 
                 CE_PER 
                 Percentage of the number of cellscorrespondingto cancer  
               
               
                   
                   
                 epithelium to that of cells existing in the entire image area 
               
               
                 P2 
                 CS_PER 
                 Percentage of thenumber of cells correspondingto cancer stroma  
               
               
                   
                   
                 to that of cells existing in the entire image area 
               
               
                 P3 
                 NE_PER 
                 Percentageof the number of cellscorrespondingto normal 
               
               
                   
                   
                 epithelium to that of cells existing in the entire image area 
               
               
                 P4 
                 NS_PER 
                 Percentage of the number of cells corresponding to normal stroma  
               
               
                   
                   
                 to that of cells existing in the entire image area 
               
               
                 P5 
                 CE_PC_PER 
                 Percentage of endothelial cells and pericyte type cells to cells 
               
               
                   
                   
                 existing in an area of cancer epithelium 
               
               
                 P6 
                 CE_PC_DEN 
                 Density of endothelial cells and pericyte type cells among cells 
               
               
                   
                   
                 existing an area of cancer epithelium 
               
               
                 P7 
                 CS_PC_PER 
                 Percentage of endothelial cells and pericyte type cells among cells 
               
               
                   
                   
                 existing in an area of cancer stroma 
               
               
                 P8 
                 CS_PC_DEN 
                 Density of endothelial cells and pericyte type cells among cells 
               
               
                   
                   
                 existing in an area of cancer stroma 
               
               
                 P9 
                 NE_PC_PER 
                 Percentage of endothelial cells and pericyte type cells to cells 
               
               
                   
                   
                 existing in an area of normal epithelium 
               
               
                 P10 
                 NE_PC_DEN 
                 Density of endothelial cells and pericyte type cells among cells 
               
               
                   
                   
                 existing in an area of normal epithelium 
               
               
                 P11 
                 NS_PC_PER 
                 Percentage of endothelial cells and pericyte type cells among cells 
               
               
                   
                   
                 existing in an area of normal stroma 
               
               
                 P12 
                 NS_PC_DEN 
                 Density of endothelial cells and pericyte type cells among cells 
               
               
                   
                   
                 existing in an area of normal stroma 
               
               
                 P13 
                 CE_MTS_PER 
                 Percentage of cells in mitosis state among cells existing in an area  
               
               
                   
                   
                 of cancer epithelium 
               
               
                 P14 
                 CE_MTS_DEN 
                 Density of cells in mitosis state existing in an area of cancer  
               
               
                   
                   
                 epithelium 
               
               
                 P15 
                 CS_MTS_PER 
                 Percentage of cells in mitosis state among cells existing in an area  
               
               
                   
                   
                 of cancer stroma 
               
               
                 P16 
                 CS_MTS_DEN 
                 Density of cells in mitosis status existing in an area of cancer stroma 
               
               
                 P17 
                 NE_MTS_PER 
                 Percentage of cells in mitosis state among cells existing in an area  
               
               
                   
                   
                 of normal epithelium 
               
               
                 P18 
                 NE_MTS_DEN 
                 Density of cells in mitosis state existing in an area of normal 
               
               
                   
                   
                 epithelium 
               
               
                 P19 
                 NS_MTS_PER 
                 Percentage of cells in mitosis state existing in an area of normal 
               
               
                   
                   
                 stroma 
               
               
                 P20 
                 NS_MTS_DEN 
                 Density of cells in mitosis state existing in an area of normal stroma 
               
               
                 P21 
                 CE_MA_PER 
                 Percentage of macrophage type cells against cells existing in an  
               
               
                   
                   
                 area of cancer epithelium 
               
               
                 P22 
                 CE_MA_DEN 
                 Density of macrophage type cells existing in an area of cancer  
               
               
                   
                   
                 epithelium 
               
               
                 P23 
                 CS_MA_PER 
                 Percentage of macrophage type cells existing in an area of cancer  
               
               
                   
                   
                 stroma 
               
               
                 P24 
                 CS_MA_DEN 
                 Density of macrophage type cells existing in an area of cancer  
               
               
                   
                   
                 stroma 
               
               
                 P25 
                 NE_MA_PER 
                 Percentage of macrophage type cells existing in an area of normal 
               
               
                   
                   
                 epithelium 
               
               
                 P26 
                 NE_MA_DEN 
                 Density of macrophage type cells existing in an area of normal 
               
               
                   
                   
                 epithelium 
               
               
                 P27 
                 NS_MA_PER 
                 Percentage of macrophage type cells existing in an area of normal 
               
               
                   
                   
                 stroma 
               
               
                 P28 
                 NS_MA_DEN 
                 Density of macrophage type cells existing in an area of normal 
               
               
                   
                   
                 stroma 
               
               
                 P29 
                 CE_TIL_PER 
                 Percentage of lymphoplasma cell type cells existing in an area of 
               
               
                   
                   
                 cancer epithelium 
               
               
                 P30 
                 CE_TIL_DEN 
                 Density of lymphoplasma cell type cells existing in an area of 
               
               
                   
                   
                 cancer epithelium 
               
               
                 P31 
                 CS_TIL_PER 
                 Percentage of lymphoplasma cell Type cells existing in an area of 
               
               
                   
                   
                 cancer stroma 
               
               
                 P32 
                 CS_TIL_DEN 
                 Density of lymphoplasma cell type cells existing in an area of 
               
               
                   
                   
                 cancer stroma 
               
               
                 P33 
                 NE_TIL_PER 
                 Percentage of lymphoplasma cell type cells existing in an area of 
               
               
                   
                   
                 normal epithelium 
               
               
                 P34 
                 NE_TIL_DEN 
                 Density of lymphoplasma cell type cells existing in an area of 
               
               
                   
                   
                 normal epithelium 
               
               
                 P35 
                 NS_TIL_PER 
                 Percentage of lymphoplasma cell type cells existing in an area of 
               
               
                   
                   
                 normal stroma 
               
               
                 P36 
                 NS_TIL_DEN 
                 Density of lymphoplasma cell type cells existing in an area of 
               
               
                   
                   
                 normal stroma 
               
               
                 P37 
                 CE_FB_PER 
                 Percentage of fibroblast type cells existing in an area of cancer 
               
               
                   
                   
                 epithelium 
               
               
                 P38 
                 CE_FB_DEN 
                 Density of fibroblast type cells existing in a region of cancer 
               
               
                   
                   
                 epithelium 
               
               
                 P39 
                 CS_FB_PER 
                 Percentage of fibroblast type cells existing in an area of cancer 
               
               
                   
                   
                 stroma 
               
               
                 P40 
                 CS_FB_DEN 
                 Density of fibroblast type cells existing in an area of cancer stroma 
               
               
                 P41 
                 NE_FB_PER 
                 Percentage of fibroblast type cells existing in an area of normal 
               
               
                   
                   
                 epithelium 
               
               
                 P42 
                 NE_FB_DEN 
                 Density of fibroblast type cells existing in an area of normal 
               
               
                   
                   
                 epithelium 
               
               
                 P43 
                 NS_FB_PER 
                 Percentage of fibroblast type cells existing in an area of normal 
               
               
                   
                   
                 stroma 
               
               
                 P44 
                 NS_FB_DEN 
                 Density of fibroblast type cells existing in an area of normal stroma 
               
               
                 P45 
                 CE_N1_PER 
                 Percentage of cells in nuclear grade 1 state existing in an area of 
               
               
                   
                   
                 cancer epithelium 
               
               
                 P46 
                 CE_N1_DEN 
                 Density of cells in nuclear grade 1 state existing in an area of  
               
               
                   
                   
                 cancer epithelium 
               
               
                 P47 
                 CE_N2_PER 
                 Percentage of cells in nuclear grade 2 state existing in an area of 
               
               
                   
                   
                 cancer epithelium 
               
               
                 P48 
                 CE_N2_DEN 
                 Density of cells in nuclear grade 2 state existing in an area of  
               
               
                   
                   
                 cancer epithelium 
               
               
                 P49 
                 CE_N3_PER 
                 Percentage of cells in nuclear grade 3 state existing in an area of 
               
               
                   
                   
                 cancer epithelium 
               
               
                 P50 
                 CE_N3_DEN 
                 Density of cells in nuclear grade 3 state existing in an area of  
               
               
                   
                   
                 cancer epithelium 
               
               
                 P51 
                 CE_TB_DEN_CNT 
                 Density of the number of tubule formation tissue type cells existing 
               
               
                   
                   
                 in an area of cancer epithelium 
               
               
                 P52 
                 CE_TB_DEN_AREA 
                 Density of area of tubule formation tissue type cells existing in an 
               
               
                   
                   
                 area of cancer epithelium 
               
               
                 P53 
                 CE_DCIS_DEN_CNT 
                 Density of the number of ductal carcinoma in situ (DCIS) tissue 
               
               
                   
                   
                 type cells existing in an area of cancer epithelium 
               
               
                 P54 
                 CE_DCIS_DEN_AREA 
                 Density of a region of ductal carcinoma in situ (DCIS) tissue type 
               
               
                   
                   
                 cells existing in an area of cancer epithelium 
               
               
                 P55 
                 CE_BV_DEN_CNT 
                 Density of the number of cells corresponding to blood vessel 
               
               
                   
                   
                 existing in an area of cancer epithelium 
               
               
                 P56 
                 CE_BV_DEN_AREA 
                 Density of the cell area corresponding to blood vessel existing in  
               
               
                   
                   
                 an area of cancer epithelium 
               
               
                 P57 
                 CS_BV_DEN_CNT 
                 Density of the number of cells corresponding to blood vessel 
               
               
                   
                   
                 existing in an area of cancer stroma 
               
               
                 P58 
                 CS_BV_DEN_AREA 
                 Density of the cell area corresponding to blood vessel existing in  
               
               
                   
                   
                 an area of cancer stroma 
               
               
                 P59 
                 NE_BV_DEN_CNT 
                 Density of the number of cells corresponding to blood vessel 
               
               
                   
                   
                 existing in an area of normal epithelium area 
               
               
                 P60 
                 NE_BV_DEN_AREA 
                 Density of cell area corresponding to blood vessel existing in an 
               
               
                   
                   
                 area of normal epithelium 
               
               
                 P61 
                 NS_BV_DEN_CNT 
                 Density of the number of cells corresponding to blood vessel 
               
               
                   
                   
                 existing in an area of normal stroma 
               
               
                 P62 
                 NS_BV_DEN_AREA 
                 Density of cell area corresponding to blood vessel existing in an 
               
               
                   
                   
                 area of normal stroma 
               
               
                 P63 
                 N1_PER 
                 Percentage of the number of cells in nuclear grade 1 state to that  
               
               
                   
                   
                 of cells existing in the entire image area 
               
               
                 P64 
                 N2_PER 
                 Percentage of the number of cells in nuclear grade 2 state to that  
               
               
                   
                   
                 of cells existing in the entire image area 
               
               
                 P65 
                 N3_PER 
                 Percentage of the number of cells in nuclear grade 3 state to that  
               
               
                   
                   
                 of cells existing in the entire image area 
               
               
                   
               
            
           
         
       
     
     Hereinafter, a description of the pathomics data manager  110  will be followed. 
     The pathomics data manager  110  preprocesses input pathomics raw data  2  and stores the preprocessed pathomics data. 
     The pathomics data manager  110  may classify parameters constituting the pathomics data into tissue information and cell information, and may remove quantitative data of information on a cell type that cannot exist in a tissue or on features that are not discovered, from each pathomics data, based on a relationship table between tissue information and cell information. 
     For example, the relationship table between tissue information and cell information is composed of a relationship matrix between tissue and cells as shown in Table 2, and information of cells to be removed from each tissue is mapped thereto. In Table 2, the tissue information is written on the horizontal axis. Here, CE stands for cancer epithelium, CS stands for cancer stroma, NE stands for normal epithelium, NS stands for normal stroma, N stands for necrosis, and F stands for Fat. In Table 2, the cell information is written in the vertical axis. Here, PC stands for Endothelial cell and pericyte, MTS stands for mitosis, MA stands for macrophage, TIL stands for lymphoplasma cell, FB stands for fibroblast, N1 stands for nuclear grade 1, N2 stands for nuclear grade 2, N3 stands for nuclear grade 3, TB stands for tubule formation, DCIS stands for ductal carcinoma in situ (DCIS), NV stands for nerve, and BV stands for blood vessel. 
     
       
         
           
               
               
               
               
               
               
               
             
               
                 TABLE 2 
               
               
                   
               
               
                 Tissue cell 
                 CE 
                 CS 
                 NE 
                 NS 
                 N 
                 F 
               
               
                   
               
             
            
               
                 PC 
                   
                   
                   
                   
                 x 
                 x 
               
               
                 MTS 
                   
                   
                   
                   
                 x 
                 x 
               
               
                 MA 
                   
                   
                   
                   
                 x 
                 x 
               
               
                 TIL 
                   
                   
                   
                   
                 x 
                 x 
               
               
                 FB 
                   
                   
                   
                   
                 x 
                 x 
               
               
                 N1 
                   
                 x 
                 x 
                 x 
                 x 
                 x 
               
               
                 N2 
                   
                 x 
                 x 
                 x 
                 x 
                 x 
               
               
                 N3 
                   
                 x 
                 x 
                 x 
                 x 
                 x 
               
               
                 TB 
                   
                 x 
                 x 
                 x 
                 x 
                 x 
               
               
                 DCIS 
                   
                 x 
                 x 
                 x 
                 x 
                 x 
               
               
                 NV 
                 x 
                 x 
                 x 
                 x 
                 x 
                 x 
               
               
                 BV 
                   
                   
                   
                   
                 x 
                 x 
               
               
                   
               
            
           
         
       
     
     Cancer cells are very rare in an adipose tissue. Accordingly, the number of cells annotated with information about nuclear grade may be wrong or not helpful for predicting the features of carcinoma at all. Therefore, if cell feature values (that is, PC, MTS, BV, etc.) are counted on the adipose tissue F in the pathomics raw data, the pathomics data manager  110  removes the corresponding values referring to Table 2. If feature values of target cell to be removed are counted on tissues (CE, CS, NE, NS, N) classified from each pathomics raw data, the pathomics data manager  110  removes the corresponding values as the case of the adipose tissue F. 
     Additionally, the pathomics data manager  110  may remove a parameter having a small count value from the pathomics raw data. In pathomics data that is quantitative data, since a very small value affects statistical analysis due to a fold having a large variation, the pathomics data manager  110  filters out cell feature values with meaningless distributions or small values. The pathomics data manager  110  may find a cell feature corresponding to an outlier in the entire sample, for example, in the way of count per million (CPM). 
     The pathomics data manager  110  calculates representative values of individual data constituting the pathomics data, by using pathomics data obtained through preprocessing each pathomics raw data  2 . The individual pathomics data may be the number of specific cells or tissues, or the number of pixels of specific cells or tissues. The specific cells or tissues may be, for example, endothelial cell and pericyte, and mitosis (MTS). Further, the individual pathomics data simply may be a single parameter constituting the pathomics data and may be referred to as a “p (pathomics) feature” or a “p feature cell” in the description. 
     It is assumed that a plurality of samples (e.g., K samples) is input to pathomics data manager  110 . Then, the pathomics data manager  110  calculates a representative value representing K samples for each p feature. 
     The way the pathomics data manager  110  calculates a representative value for each p feature may be various. For example, the pathomics data manager  110  may use a relative log cell-count (RLC)-based data normalization method. An expected p feature value E[Y pk ] of k samples among K samples may be defined by Equation 1. 
     
       
         
           
             
               
                 
                   
                     
                       E 
                        
                       
                         [ 
                         
                           Y 
                           pk 
                         
                         ] 
                       
                     
                     = 
                     
                       
                         
                           μ 
                           pk 
                         
                         
                           s 
                           k 
                         
                       
                        
                       
                         N 
                         k 
                       
                     
                   
                    
                   
                     
 
                   
                    
                   
                     
                       S 
                       k 
                     
                     = 
                     
                       
                         ∑ 
                         
                           p 
                           = 
                           1 
                         
                         P 
                       
                        
                       
                         μ 
                         pk 
                       
                     
                   
                 
               
               
                 
                   ( 
                   
                     Equation 
                      
                     
                         
                     
                      
                     1 
                   
                   ) 
                 
               
             
           
         
       
     
     In Equation 1, Y pk  is a count level of p feature cells measured in k samples (pathological image), and E[Y pk ] is an distribution of p feature cells expected from Y pk . N k  is a count level of all cells or pixels measured in k samples. μ pk  is a correct answer and an actual count level of p feature cells for unknowable K samples. S k  is an actual count level of all cells for k samples. 
     A pseudo-reference Y p   RLC  representing K samples may be defined by Equation 2. In Equation 2, r is a biological replicate. In Equation 2, X prk  is a count of p feature and r for k samples. 
     
       
         
           
             
               
                 
                   
                     Y 
                     p 
                     RLC 
                   
                   = 
                   
                     
                       
                         Π 
                         
                           k 
                           = 
                           1 
                         
                         K 
                       
                        
                       
                         Π 
                         
                           r 
                           = 
                           1 
                         
                         R 
                       
                        
                       
                         X 
                         prk 
                       
                     
                     kr 
                   
                 
               
               
                 
                   ( 
                   
                     Equation 
                      
                     
                         
                     
                      
                     2 
                   
                   ) 
                 
               
             
           
         
       
     
     The pathomics data manager  110  may normalize p feature value, through dividing the p feature value X prk  by a scaling factor Y p   RLC . The scaling factor makes a distribution of quantitative data be normalized. 
     The pathomics data manager  110  may remove left skewed characteristic from the count data by posing Log 2 ( ) on the normalized p feature representative value. 
     Through the above-described processes, the pathomics data manager  110  generates pathomics representative data  4  which represents the pathomics data including K samples. The pathomics representative data  4  may be expressed as a set of p features, and each p feature has a representative value which is a quantitative data. 
     Next, a description about the genetic information manager  120  will be followed. 
     The genetic information manager  120  may remove down-regulated genes from all gene samples. The genetic information manager  120  may find cell feature corresponding to an outlier sample in all samples, by a count per million (CPM) method. If a gene having a CPM value less than 1 is more than or equal to half of all samples, the gene may be defined as a down-regulated gene and may be excluded. In other words, in the genetic information (e.g., RNA sequence) that is quantitative data, since a very small value affects statistical analysis, the corresponding value is analytically filtered out. The CPM (C gk ) of g gene of the k-th sample may be defined by Equation 3. 
         C   gk =(μ gk   /Y   gk )*1000000  (Equation 3)
 
     In Equation 3, Y gk  is a read count of g gene in k samples, and μ gk  is an expression level of the g gene in k samples. 
     The genetic information manager  120  extracts genetic information from a plurality of samples (e.g., K samples). Here, an arbitrary specific gene may be referred to as “g gene”. The genetic information manager  120  may utilize various techniques to calculate information of the g gene. 
     The genetic information manager  120  may use various data normalization methods to obtain the genetic information of the g gene. For example, at least one of a data normalization technique based on relative log-expression (RLE) and a data normalization technique based on trimmed mean of M value may be used. 
     According to an embodiment, the genetic information manager  120  may use a data normalization technique based on relative log-expression (RLE). An expected g expression value E[Y gk ] in k samples of the K samples may be defined by Equation 4. Since Y gk  is the number of read counts of the g gene measured in k samples and is merely a partial sequence read count, it is possible to predict the actual expression value E[Y gk ] from Y gk . 
     
       
         
           
             
               
                 
                   
                     
                       E 
                        
                       
                         [ 
                         
                           Y 
                           gk 
                         
                         ] 
                       
                     
                     = 
                     
                       
                         
                           
                             μ 
                             gk 
                           
                            
                           
                             L 
                             g 
                           
                         
                         
                           s 
                           k 
                         
                       
                        
                       
                         N 
                         k 
                       
                     
                   
                    
                   
                     
 
                   
                    
                   
                     
                       S 
                       k 
                     
                     = 
                     
                       
                         ∑ 
                         
                           g 
                           = 
                           1 
                         
                         G 
                       
                        
                       
                         
                           μ 
                           gk 
                         
                          
                         
                           L 
                           g 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   
                     Equation 
                      
                     
                         
                     
                      
                     4 
                   
                   ) 
                 
               
             
           
         
       
     
     In Equation 4, L g  is a length of the g gene, and N k  is the number of read counts of the entire gene measured in k samples. 
     A pseudo-reference Y g   RLE  representing K samples may be defined by Equation 5. In Equation 5, r is biological replicate, and X grk  is a read count for the g gene and r in k samples. 
     
       
         
           
             
               
                 
                   
                     Y 
                     g 
                     RLE 
                   
                   = 
                   
                     
                       
                         ∏ 
                         
                           k 
                           = 
                           1 
                         
                         K 
                       
                        
                       
                         
                           
                             ∏ 
                             
                               r 
                               = 
                               1 
                             
                           
                           R 
                         
                          
                         
                           x 
                           grk 
                         
                       
                     
                     kr 
                   
                 
               
               
                 
                   ( 
                   
                     Equation 
                      
                     
                         
                     
                      
                     5 
                   
                   ) 
                 
               
             
           
         
       
     
     The genetic information manager  120  may normalize a distribution of g expression value by dividing the g expression value X grk  with a scaling factor Y g   RLE . The scaling factor has an effect of normalizing a distribution of quantitative data. 
     According to another embodiment of the present disclosure, the genetic information manager  120  may use a normalization technique based on trimmed mean of M value. Among the genetic information, RNA-sequencing data is composed of reads. The sizes of gene samples are different, and each gene has different library composition. Thus, the genetic information manager  120  may normalize the size of the gene samples. 
     First, the genetic information manager  120  selects a reference sample K ‘ among K samples. Then, the genetic information manager  120  obtains an M-value M g  corresponding to log-fold for the reference sample K’, for all of K samples. For example, M g  may be defined by Equation 6. 
     
       
         
           
             
               
                 
                   
                     M 
                     g 
                   
                   = 
                   
                     
                       log 
                       2 
                     
                      
                     
                       
                         
                           Y 
                           gk 
                         
                         / 
                         
                           N 
                           k 
                         
                       
                       
                         
                           Y 
                           
                             gk 
                             ′ 
                           
                         
                         / 
                         
                           N 
                           
                             k 
                             ′ 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   
                     Equation 
                      
                     
                         
                     
                      
                     6 
                   
                   ) 
                 
               
             
           
         
       
     
     The genetic information manager  120  obtains an A-value A g  corresponding to a geometric mean of the reference sample K′ and the k-th sample. The A value A g , for example, may be defined by Equation 7. The A value A g  may be defined by an absolute expression level. 
         A   g =½log 2 ( Y   gk   /N   k   *Y   gk′   /N   k′ )  (Equation 7)
 
     M-value M g  being a log fold change is a reference value for finding a biased gene, and A-value A g  being a geometric mean is a reference value for finding up-regulated/down-regulated genes. The genetic information manager  120  may remove genes that fall within the upper/lower 30% of the M-value and genes having upper 5% of A-value, and determine a scaling value normalizing the size of the gene samples through the remaining genes. That is, the genetic information manager  120  may determine a scaling factor by using a trimmed mean, and normalize the size of each gene sample by dividing the library size of each gene sample with the scaling factor. 
     So far, two data normalization techniques based on relative log-expression (RLE) and based on trimmed mean of M value have been described as examples of data normalization techniques used by the genetic information manager  120 . To select which of the two techniques depends on the number of independent variables. The data normalization technique based on RLE may be used for data having a small number of independent variables, and a data normalization technique based on trimmed mean of M value may be used for data affected by outlier values due to having a large number of independent variables. 
     Through such a procedure, the genetic information manager  120  generates genetic information  5  from the genetic information of the K samples. Genetic information may be expressed as a set of g genes. 
     Hereinafter, a description of the gene module generator  130  will be followed. 
     The gene module generator  130  receives the gene information  5  generated by the genetic information manager  120 . The gene module generator  130  generates at least one gene module related to the genetic information  5  by using quantitative data of RNAs and/or proteins included in the genetic information  5 . A gene module is a group containing correlated genes or a group containing genes having similar functions. Further, the gene module may be composed of a single RNA/single protein. The gene module generator  130  may give a biological and/or medical meaning to the gene module through biological and/or medical information annotated to multiple genes included in each gene module. 
     The gene modules may be generated in various ways. According to an embodiment, based on a statistical technique, the gene module generator  130  searches for a correlation network of data included in the genetic information  5  using De-novo, whereby correlated genes may be modularized into a same group. According to another embodiment, the gene module generator  130  may extract correlated genes based on unsupervised machine learning and may modularize the extracted genes into a same group. According to still another embodiment, the gene module generator  130  may use gene function groups defined in an external database. That is, a plurality of gene modules exists in the form of a predefined functional group, and the gene module generator  130  may extracts at least one gene module including genes contained in the gene information  5  from the plurality of gene modules. 
     Hereinafter, an example of an extraction method of a gene module through a correlation network will be described. 
     First, the gene module generator  130  generates a correlation network connecting genes based on interactions of the genes included in the genetic information  5 . A node in the correlation network is a gene, and an edge represents an interaction between connected genes. Interactions among all genes may be determined by pairwise-correlation between two genes. For example, gene interactions (dependencies) may be confirmed through rank correlations such as Pearson&#39;s correlation coefficient, Sperman&#39;s rank coefficient, Kendall tau rank correlation, and the like. An equation a ij =|cor(x i x j )| β  (here, i and j are indices of genes) represents a correlation between genes when using a correlation threshold of β, and the interactions among n genes may be calculated with an n×n matrix, if the total number of genes is n. 
     Gene module generator  130  makes clusters of genes having the same functions in the correlation network. Since a gene or a protein with a large topological overlap value is known to have a high probability of having the same functions, the gene module generator  130  may extract genes having the same function by calculating the topological overlap value in the correlation network. The topological overlap value corresponds to interconnectedness between two genes. The topological overlap value t ij  of the i-gene and j-gene may be calculated by Equation 8. 
     
       
         
           
             
               
                 
                   
                     t 
                     ij 
                   
                   = 
                   
                     
                       
                          
                         
                           
                             
                               N 
                               1 
                             
                              
                             
                               ( 
                               i 
                               ) 
                             
                           
                           ⋂ 
                           
                             
                               N 
                               1 
                             
                              
                             
                               ( 
                               j 
                               ) 
                             
                           
                         
                          
                       
                       + 
                       
                         a 
                         ij 
                       
                     
                     
                       
                         min 
                          
                         
                           { 
                           
                             
                                
                               
                                 
                                   N 
                                   1 
                                 
                                  
                                 
                                   ( 
                                   i 
                                   ) 
                                 
                               
                                
                             
                             , 
                             
                                
                               
                                 
                                   N 
                                   1 
                                 
                                  
                                 
                                   ( 
                                   j 
                                   ) 
                                 
                               
                                
                             
                           
                           } 
                         
                       
                       + 
                       1 
                       - 
                       
                         a 
                         ij 
                       
                     
                   
                 
               
               
                 
                   ( 
                   
                     Equation 
                      
                     
                         
                     
                      
                     8 
                   
                   ) 
                 
               
             
           
         
       
     
     In Equation 8, when i and j are equal (that is, i=j), “a” is 1. N 1 (i) refers to genes directly connected to the i gene (gene nodes having a distance of 1 from i gene node), and |⋅| means the number of included genes. 
     The gene module generator  130  generates a gene module by clustering genes with a high probability of having the same function, by using a topological overlap value. Here, the gene module generator  130  calculates a distance D ij  between two genes based on the interconnection value t ij  between the two genes obtained by the topological overlap, and performs hierarchical clustering for the genes based on the distance. Through clustering, a plurality of gene modules may be generated. Various techniques such as k-means clustering, consensus clustering, and the like, may be used for clustering. 
     The gene module generator  130  extracts representative information of the plurality of gene modules. The gene module generator  130  may extract representative information representing genes existing in each gene module, by using principal component analysis (PCA). The representative information of each gene module may be a first PCA vector, which may be defined as an eigengene of each gene module. 
     When a plurality of gene modules related to the gene information  5  is determined, the gene module generator  130  determines biological functions significantly enriched in each gene module through functional enrichment analysis. Additionally, when a plurality of gene modules related to the gene information  5  is determined, the gene module generator  130  may add biological information and medical information describing each gene module with reference to accessible databases and literature. 
     First, the gene module generator  130  may extract a specific function in which the representative information of each gene module is significantly enriched, among functions defined in an external database. Here, the gene module generator  130  may use gene set enrichment analysis (GSEA). For example, from external databases of gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG), the gene module generator  130  may extract functions of gene ontology (e.g., immune response, immune system process, etc.) and KEG functions (e.g., cytokine-cytokine receptor interaction, etc.), where any gene module is significantly enriched. 
     The gene module generator  130  may perform significance test on association of the extracted specific function corresponding to each gene module. Here, various significance test method such as Fisher&#39;s exact test, chi square test, cochran test, and the like may be used. If the functions extracted corresponding to each gene module are plural, the gene module generator  130  may annotate a plurality of functions to the corresponding gene module, and set a representative function that is displayed preferentially. 
     For example, the plurality of gene modules may be coded with color names, and mapped to functional information, as shown in Table 3. 
     
       
         
           
               
               
               
               
             
               
                 TABLE 3 
               
               
                   
               
               
                   
                   
                 Classified genetic 
                   
               
               
                   
                   
                 information 
                   
               
               
                 No. 
                 Gene module 
                 (Example) 
                 Function 
               
               
                   
               
             
            
               
                 M1 
                 Black 
                 SPNS2, FAM153A, 
                 immune response, immune system 
               
               
                   
                   
                 RRN3P1, ZNF57, 
                 process, regulation of immune system 
               
               
                   
                   
                 BHLHE22, NCF1C, 
                 process, defense response, leukocyte 
               
               
                   
                   
                 SCML4, LILRB1, GM2A, 
                 activation 
               
               
                   
                   
                 SYAP1 
                   
               
               
                 M2 
                 Yellow 
                 MYLK2, FBX043, 
                 mitotic cell cycle, mitotic cell cycle 
               
               
                   
                   
                 GDPD2, GOLT1B, 
                 process, cell cycle, cell cycle process, 
               
               
                   
                   
                 WHAMML2, NHLH2, 
                 chromosome organization 
               
               
                   
                   
                 CABLES2, PBK, CEP152, 
                   
               
               
                   
                   
                 LAMB2 
                   
               
               
                 M3 
                 Yellowgreen 
                 IF144, HSH2D, IL22RA1, 
                 response to virus, defense response to 
               
               
                   
                   
                 STAT2, RTP4, OASL, 
                 virus, innate immune response, type I 
               
               
                   
                   
                 TRAFD1, IFIT1, ISG15, 
                 interferon signaling pathway, cellular 
               
               
                   
                   
                 DHX58 
                 response to type I interferon 
               
               
                 M4 
                 Magenta 
                 COL11A2, HIF3A, 
                 tissue development, single-multicellular 
               
               
                   
                   
                 KRT81, ITGB8, C4BPA, 
                 organism process, anatomical structure 
               
               
                   
                   
                 EPHB1, XDH, SYNM, 
                 development, epidermis development, 
               
               
                   
                   
                 KLK8, IFF02 
                 multicellular organismal process 
               
               
                 M5 
                 Lightgreen 
                 GPR176, LPHN2, 
                 homophilic cell adhesion via plasma 
               
               
                   
                   
                 PCDH18, CDKL1, STL, 
                 membrane adhesion molecules, cell-cell 
               
               
                   
                   
                 ENTPD1, FILIP1, ITGAV, 
                 adhesion via plasma-membrane adhesion 
               
               
                   
                   
                 UTRN, KLF12 
                 molecules, movement of cell or 
               
               
                   
                   
                   
                 subcellular component, vasculature 
               
               
                   
                   
                   
                 development, blood vessel development 
               
               
                 M6 
                 Pink 
                 MTMR11, CHST6, 
                 extracellular matrix organization, 
               
               
                   
                   
                 FILIP1L, F13A1, ABCG4, 
                 extracellular structure organization, 
               
               
                   
                   
                 FNDC4, ISM1, LPAR1, 
                 multicellular organism development, 
               
               
                   
                   
                 ANAPC5, CCBE1 
                 single-multicellular organism process, 
               
               
                   
                   
                   
                 system development 
               
               
                 M7 
                 Cyan 
                 SEMA3G, HTR2B, 
                 single-multicellular organism process, 
               
               
                   
                   
                 ABCB1, PRELP, 
                 vasculature development, circulatory 
               
               
                   
                   
                 ARHGAP6, CAPN11, 
                 system development, cardiovascular 
               
               
                   
                   
                 ZCCHC24, DNASE1L3, 
                 system development, blood vessel 
               
               
                   
                   
                 HOXA7, GNAL 
                 development 
               
               
                 M8 
                 Violet  
                 KY, SPOCK3, PIK3C2G, 
                 anterograde trans-synaptic signaling, 
               
               
                   
                   
                 TNS4, CLDN19, TRPM3, 
                 synaptic signaling, trans-synaptic 
               
               
                   
                   
                 KLHL29, ALX4, 
                 signaling, chemical synaptic trans- 
               
               
                   
                   
                 TP53AIP1, TEPP 
                 mission, nervous system development 
               
               
                 M9 
                 darkslateblue 
                 HIST2H2BA, HIST1H3G, 
                 Systemic lupus erythematosus, 
               
               
                   
                   
                 HIST1H2BG, HIST1H1E, 
                 nucleosome organization, nucleosome 
               
               
                   
                   
                 HIST1H4H, HIST1H1D, 
                 assembly, chromatin assembly or 
               
               
                   
                   
                 HIST1H2BE, 
                 disassembly, Alcoholism 
               
               
                   
                   
                 HIST1H2BH, 
                   
               
               
                   
                   
                 HIST1H2BD, HIST1H1C 
                   
               
               
                 M10 
                 Orange 
                 TMEM196, RPS4Y1, 
                 regulation of wound healing, regulation  
               
               
                   
                   
                 GCG, MOGAT3, 
                 of response to wounding, inorganic  
               
               
                   
                   
                 UGT2A3, REG1B, 
                 anion transport, negative  
               
               
                   
                   
                 AP0A2, CDH9, 
                 regulation of wound healing,  
               
               
                   
                   
                 NCRNA00230B, 5T85IA3 
                 triglyceride metabolic process 
               
               
                 M11 
                 Blue 
                 PBXIP1, RNF13, PRKCZ, 
                 cellular metabolic process, metabolic 
               
               
                   
                   
                 DDAH2, ZNF273, UBTF, 
                 process, cellular macromolecule 
               
               
                   
                   
                 CC2D1A, BBC3, SFTPD, 
                 metabolic process, primary metabolic 
               
               
                   
                   
                 USF2 
                 process, organic substance metabolic 
               
               
                   
                   
                   
                 process 
               
               
                 M12 
                 Darkturquoise 
                 NEU1, PPP1R11, YIF1B, 
                 cellular nitrogen compound metabolic 
               
               
                   
                   
                 CCDC86, MRPS18A, 
                 process, mitochondrial translation, 
               
               
                   
                   
                 UQCRFS1, RTN4IP1, 
                 mitochondrial translational elongation, 
               
               
                   
                   
                 MRP522, GNL1, WDR77 
                 mitochondrial translational termination, 
               
               
                   
                   
                   
                 gene expression 
               
               
                 M13 
                 royalblue 
                 RPL36, EEF2, RPL15, 
                 SRP-dependent cotranslational protein 
               
               
                   
                   
                 HNRNPA1, EIF3M, 
                 targeting to membrane, cotranslational 
               
               
                   
                   
                 RPS14, RPS27, RPL14, 
                 protein targeting to membrane, protein 
               
               
                   
                   
                 RPS11, RPL10 
                 targeting to ER, establishment of protein 
               
               
                   
                   
                   
                 localization to endoplasmic reticulum, 
               
               
                   
                   
                   
                 nuclear-transcribed mRNA catabolic 
               
               
                   
                   
                   
                 process, nonsense-mediated decay 
               
               
                 M14 
                 Brown 
                 ATL2, PVRL1, ILDR1, 
                 ion transport, transmembrane transport, 
               
               
                   
                   
                 NCRNA00094, ARL14,  
                 ion transmembrane transport, cell 
               
               
                   
                   
                 NUAK2, FAM47E, 
                 projection organization, cell projection 
               
               
                   
                   
                 TMEM144, LRGUK, 
                 morphogenesis 
               
               
                   
                   
                 KATNA1 
                   
               
               
                 M15 
                 Darkgrey 
                 FAM171A2, TMED8, 
                 protein localization, cellular localization, 
               
               
                   
                   
                 ZNF20, MAGED1, VEZT, 
                 establishment of localization in cell, 
               
               
                   
                   
                 DTNB, ARHGEF3, 
                 protein transport, organic substance 
               
               
                   
                   
                 CYP2D6, FBX017, 
                 transport 
               
               
                   
                   
                 SNX14 
                   
               
               
                 M16 
                 bisque4 
                 DUSP1, TRIB1, EGR4, 
                 positive regulation of cellular process, 
               
               
                   
                   
                 GADD45B, KLF4, 
                 cellular response to chemical stimulus, 
               
               
                   
                   
                 CYR61, HBEGF, HAS1, 
                 negative regulation of cellular metabolic 
               
               
                   
                   
                 PPP1R15A, NR4A1 
                 process, regulation of cellular 
               
               
                   
                   
                   
                 macromolecule biosynthetic process, 
               
               
                   
                   
                   
                 positive regulation of cellular metabolic 
               
               
                   
                   
                   
                 process 
               
               
                   
               
            
           
         
       
     
     Hereinafter, a description of the connector  150  will be followed. 
     The connector  150  extracts relationships between the representative pathomics data and the plurality of gene modules, by using various techniques. Here, the representative pathomics data is composed of a plurality of individual pathomics data, and a value of each individual pathomics data has a representative value of a plurality of samples. 
     The connector  150  may calculate a correlation between the representative information of the gene modules and the representative pathomics data. In this case, the representative information of the gene modules is information shortened in a designated manner, and may be shortened by various statistical methods such as an average value analysis of genes included in each gene module, a PCA, a centroid, an eigengene, and the like. The connector  150  may calculate correlations through correlation techniques such as Pearson, Spearman, kendall, and the like. 
     The connector  150  may determine existence of relationship between individual pathomics data and each gene module, by comparing a one-to-one relationship value between the individual pathomics data and each gene module with a threshold value (e.g., p-value). In addition to the relationship value calculated with the correlation, the connector  150  may determine the existence of the relationship between individual pathomics data and each gene module through an unsupervised clustering technique. The unsupervised clustering technique may be, for example, hierarchical clustering, consensus clustering, non-negative matrix factorization, and the like. 
     For example, the connector  150  may determine that each of the individual pathomics data CE_TIL_DEN and CS_TIL_DEN has a positive relationship (for example, a relationship value of 0.42 and 0.35, respectively) with a gene module corresponding to immune response and immune system process (for example, coded with a color name of black). Then, the connector  150  connects each of the individual pathomics data CE_TIL_DEN and CS_TIL_DEN with the gene module corresponding to immune response and immune system process. Further, the individual pathomics data may be connected to a plurality of gene modules. 
     Next, a description of the interpretation information generator  170  will be followed. 
     The interpretation information generator  170  receives a connection relationship between individual pathomics data and each gene module from the connector  150 . The interpretation information generator  170  refers to biological function information and medical description information that are extracted corresponding to the gene module by the gene module generator  130 . Further, the interpretation information generator  170  maps biological function information and medical description information extracted corresponding to the gene module as interpretation information of the individual pathomics data. The interpretation information generator  170  may provide a means to interpret the meaning of the pathomics data extracted from the phtological slide as annotated information to the gene/protein, through the biological and/or medical information of the gene module associated/correlated with the pathomics data. 
     The interpretation information generator  170  may provide an interface screen that visualizes digital pathology data, a gene module, and biologically and/or medically related interpretation information. 
       FIG. 3  is an example of a relationship analysis result for connecting pathomics data and a gene module according to an embodiment, and  FIG. 4  is a diagram visually representing a connection relationship between pathomics data and a gene module according to an embodiment. 
     Referring to  FIG. 3 , the connector  150  calculates a one-to-one relationship value between a value of each gene module and individual phatomics data. The relationship value may indicate a positive or negative relationship. The connector  150  may display the relationship analysis result  20  on an interface screen. The relationship analysis result  20  is a result of correlation analysis between the pathomics data and representative information (e.g., eigenvector) of gene modules which is composed of transcript genes. In the relationship analysis result  20 , each column represents a component of the pathomics data and each row represents a gene module obtained from TCGA transcript data named with an arbitrary color. In the relationship analysis result  20 , each cell may be displayed only for a pair of pathomics data-gene module that is determined to have a significant correlation through Pearson correlation analysis. The correlation may be analyzed for data with both a positive correlation and a negative correlation. 
     Referring to the relationship analysis result  20 , it is determined that CE_TIL_DEN and CS_TIL_DEN of the digital pathology data have positive relationships (e.g., relationship values of 0.42 and 0.35, respectively) with a module encoded with a color name of black. 
     Referring to the relationship analysis result  20 , it is determined that CE_FB_DEN of the digital pathology data has positive relationships with modules coded with color names of lightgreen, pink, bisque4, and cyan, and has a negative relationship with a module encoded with a color name of yellow. 
     Each gene module coded with a color name is annotated with functional information significantly enriched in the gene module, and medical information describing each gene module. 
     For example, a gene module coded with the color name of black may be annotated with a function of immune response and immune system process of gene ontology. 
     A gene module coded with the color name of lightgreen may be annotated with a vessel development function of gene ontology. A gene module coded with the color name of pink may be annotated with angiogenesis and blood vessel development of gene ontology, which is a function related to vessel generation. 
     A gene module coded with the color name of bisque4 may be annotated with a function of cellular process metabolic process of gene ontology. A gene module coded with the color name of cyan may be annotated with an extracellular matrix organization function of gene ontology. 
     A gene module coded with a color name of saddlebrown is annotated with a function of protein folding and metabolic process of gene ontology 
     A gene module coded with the color name of yellow can be annotated with functions of cell cycle, nuclear division and DNA replication, which are functions related to cell generation of gene ontology. 
     Referring to  FIG. 4 , a connection relationship between pathomics data (shown in vertical axis, that is, Y axis) and gene modules (shown in horizontal axis, that is, X axis) may be visually displayed. Correlation values range from −0.542 to 0.491. The pathomics data may be histologic component. 
     In  FIG. 4 , a plurality of individual pathomics data that are adjacently located in the direction of Y axis may be interpreted to have similar meaning and high correlation thereamong. In addition, each gene module adjacently located in the direction of X axis may be interpreted to have similar gene expression pattern. 
       FIG. 5  and  FIG. 6  are examples of enrichment analysis results for a gene module coded with a color name of black. 
     Specifically,  FIG. 5  shows an example of enrichment analysis result  30  of a gene module coded with the color name of black. Here, the enrichment analysis of the gene module is performed for gene ontology and KEGG pathway. The term “category” means a database, and GOTERM_BP_ALL is a database of biological process term in gene ontology, and KEGG_PATHWAY is KEGG pathway database. 
     The enrichment analysis result  30  may be provided as a bar graph for biological and/or medical information that has a strong association with a gene module coded with the color name of black. 
     The enrichment analysis result  30  may be calculated as a false discovery rate (FDR) value. The gene module coded with the color name of black may be annotated as to have high relevance with immune response and immune system process of gene ontology, which are functions related to immunity Additionally, the gene module coded with the color name of black may be annotated as to be related with regulation of immune system process and defense response, and to be related to cytokine-cytokine receptor interaction, hematopoietic cell lineage, allograft rejection and the like of the KEGG pathway. 
     Referring to  FIG. 6 , the interpretation information generator  170  may provide an enrichment analysis result  31  of the gene module coded with the color name of black for various databases (categories) other than GOTERM_BP_ALL and KEGG_PATHWAY shown in  FIG. 5 . 
     As above-described, the interpretation information generator  170  provides a result indicating that the gene module coded with the color name of black is very significantly associated with the overall immune activities such as immune response, defense response of a cell, control of immune system, T cell activation, and the like, in the databases of gene ontology, KEGG pathway, and the like. 
     In fact, the gene module coded with the color name of black is a gene module where important genes responsible for human immune system are clustered. In addition, referring to  FIG. 3 , the gene module coded with the color name of black has high correlations with pathomics data CE_TIL_DEN and CS_TIL_DEN indicating immune cells (lymphoplasma) existing in the cancer epithelium and the cancer stroma region, respectively. Thus, it is confirmed that parameters (individual pathomics data) associated with immune cells in the pathomics data is related to gene modules with immunological features. 
       FIG. 7  and  FIG. 8  are example diagrams showing enrichment analysis results for a gene module coded with a color name of yellow. 
     Specifically,  FIG. 7  shows an example diagram of enrichment analysis result  32  of a gene module coded with a color name of yellow for gene ontology and KEGG pathway. The term “category” described in  FIG. 7  means a database. Here, GOTERM_BP_ALL refers to a biological process term database, and KEGG_PATHWAY refers to KEGG pathway database. 
     The enrichment analysis results  32  may be provided as a bar graph of biological and/or medical information that has a strong association with the gene module coded with the color name of yellow. 
     The enrichment analysis result  32  may be calculated as a false discovery rate (FDR) value. The gene module coded with the color name of yellow can be annotated as to be associated with mitotic cell cycle, mitotic cell cycle process, cell cycle, cell cycle process, and DNA replication of gene ontology, and to be associated with DNA replication and cell cycle of KEGG pathway. 
     Referring to  FIG. 8 , the interpretation information generator  170  may provide an enrichment analysis result  34  of a gene module coded with a color name of black for various databases (categories) besides GOTERM_BP_ALL and KEGG_PATHWAY shown in  FIG. 7 . 
     As above-described, the interpretation information generator  170  provides a result that the gene module coded with the color name of yellow is very significantly related with cell division being the most important in cancer cells, such as cell division, cycle of cell division, cell nucleus division, and the like. 
     Actually, the gene module coded with the color name of yellow is a gene module where genes related to cell division are clustered. In addition, referring back to  FIG. 3 , it can be seen that the gene module coded with the color name of yellow has a high correlation with pathomics data CE_PER and CE_PC_PER indicating the area of the cancer epithelium. This indicates that the larger the area of cancer epithelial cells becomes, the more genes/transcripts that are biologically related to the division of cancer cells get expressed. Thus, it is confirmed that parameters related to an area of cancer cell (individual pathomics data) in the pathomics data are related to gene modules with a feature of cancer cell division. 
     Hereinafter, more specific description about the enrichment analysis result of the gene module coded with the color name of yellow and databases will be followed. 
     In biological process term of gene ontology, a cell cycle associated with a yellow gene module is a biological process belonging to a term “cellular process”. Besides the cell cycle, the term “cellular process” includes cell activation, cell adhesion molecule production, cell communication, cell cycle checkpoints, and the like. In cell cycle term, cell cycle processes, meiotic cell cycles, regulation of cell cycles, and the like exist, and further a subgroup of biological process term exists. As such, the biological meanings of the pathomics data such as distribution, properties, and density of cancer cells, and the like in pathological images may be explained through biological process terms. 
     In the KEGG Pathway, a cell cycle related to the yellow gene module belongs to cell growth and death subordinate to cellular processes. Thus, relationships between various information such as disease mechanism, cell metabolism, and the like and histologic components of the pathomics data may be explained. 
     In BIOCARTA, biocarta terms associated with the yellow gene module are CDK regulation of DNA replication, cell cycle: G2/M checkpoint, role of BRCA1, BRCA2, ATR in cancer susceptibility, and the like. DNA replication and cell cycles are repeated results in gene ontology and KEGG pathway. In that the genes BRCA1 and BRCA2 are considered to be very important in breast cancer and have correlations with the pathomics data obtained from extracting histologic components by using surgical biopsy data of breast cancer patients, the result is very meaningful for explaining cancer relevance to the genes BRCA1 and BRCA2. 
     In the genetic association database (GAD), the GAD term associated with the yellow gene module is breast-cancer. The pathomics data related to the yellow gene module are parameters generally belonged to cancer epithelium (mitosis, degenerated &amp; necrotic tumor cell, macrophage, nuclear grade 3, ductal carcinoma in situ (DCIS), etc.). For the pathomics data obtained from extracting the histologic component by using surgical biopsy data of a breast cancer patient, the result is meaningful in that the very significant GAD term (p-value=1.54E-21) in the breast cancer is extracted. 
     In OMIM, the term associated with the yellow gene module is “Breast cancer, susceptibility to”. From this, it may be explained that the pathomics data obtained from extracting histologic components by using surgical biopsy data of breast cancer patients has significant relationship with a breast cancer. 
     UnitProt keywords related to the yellow gene module are cell cycle, nucleus, cell division, mitosis, and the like. Since those terms are associated with an area of cancer epithelium of breast cancer, it may be considered that the previously known knowledge is reproduced. 
     In UniProt tissue specificity, the term related to the yellow gene module is tissue corresponding to epithelium. Since the yellow gene module is highly associated with the area of cancer epithelium, extraction of tissues significantly associated with the epithelium is a very important result. 
       FIG. 9  is an example interface screen on which interpretation information is visually displayed, according to an embodiment. 
     Referring to  FIG. 9 , the interpretation information generator  170  may display a gene module associated with pathomics data of a patient and provide interpretation information annotated to the gene module, to the interface screen  40 . The interpretation information may include functional information that is biological information, descriptive information that is medical information, and the like. 
     The interface screen  40  may display pathomics data on a gene module basis and display associated gene modules on pathomics data basis. In addition, the interpretation information generator  170  may hierarchically display the gene modules based on the hierarchical structure information among the gene modules to facilitate understanding of the interpretation information related to the pathomics data. The interface screen  40  may be obtained by assigning arbitrary colors to gene modules and visualizing as a circos plot through distance. The interface screen  40  visually describes the pathomics-gene module relationship having a significant correlation in  FIG. 3 . The interface screen  40  may provide pathomics data correlated with corresponding gene module along with the representative biological and/or medical information of each genetic module. 
     The interface screen  40  may display immune-related functions (immune response &amp; immune system process) annotated to the gene module coded with the color name of black and further display information that the gene module has a positive relationship with individual pathomics data (CE_TIL_DEN, CS_TIL_DEN, etc.) 
     Therefore, it may be interpreted that the individual pathomics data (CE_TIL_DEN, CS_TIL_DEN, etc.) related to the number of lymphoplasma cells is associated with immune-related functions (immune response and immune system process). In addition, from a positive relationship, it may be inferred that the more lymphoplasma cells locates at cancer epithelial or cancer stroma in the slide image the more immunoreactivity activates. Such inference matches the relation of immune response between the number of pathologically interpretable lymphoplasma cells and biologically and/or medically interpretable cells. Thus, reliability of the analysis result of the AI pathology analyzer  10  may be evaluated based on the degree of match. 
     The interface screen  40  displays cell cycle, nuclear division, and DNA replication function that are annotated to the gene module coded with the color name of yellow. For example, information that there are positive relationships with CE_MA_DEN, CS_MA_DEN, CE_PER, and the like, and a negative relationship with CE_FB_DEN may be displayed together. 
     Therefore, patients with a large area of cancer in a slide image may be interpreted that the cancer cells are rapidly divided due to biologically fast cell cycle and have aggressive properties. Such an interpretation is consistent with a pathological interpretation, in that the rapid cancer cell division induces fast enlarging the size of a tumor and corresponding area of the slide image should be found to be large. Therefore, it may be verified that the size of pathologically interpretable tumor and the biological cell cycle are related features. 
       FIG. 10  is a flowchart showing a method for providing interpretation information of pathomics data according to an embodiment. 
     Referring to  FIG. 10 , an interpretation information providing system  100  receives pathomics data samples analyzed from slide images of patients (S 110 ). The pathomics data samples includes quantitative data that is obtained by digitizing features of the slide images as the number of lymphoplasama cells located in the cancer epithelial and cancer stroma of the slide image, total amount of cancer epithelial and cancer stroma, and the like. The pathomics data samples may be raw data received from the AI pathology analyzer  10 . 
     The interpretation information providing system  100  receives gene samples of the patients who provided the slide images (S 120 ). Each gene sample may include RNA information and/or protein information, which are expression products of the gene, and include expression information of RNA and/or protein. The gene samples may include RNA expression data measured by transcriptomics techniques or protein expression data measured by proteomics techniques. 
     The interpretation information providing system  100  generates pathomics representative data representing the pathomics data samples (S 130 ). The interpretation information providing system  100  calculates a representative value of individual pathomics data (p feature) constituting the pathomics data, by using the quantitative data included in the pathomics data samples. The interpretation information providing system  100  may determine a p-feature value representing K samples using, for example, a relative log cell-count (RLC) based data normalization technique. 
     The interpretation information providing system  100  generates genetic information from gene samples (S 140 ). The interpretation information providing system  100  may calculate quantitative data of an individual gene (g gene) constituting the genetic information by using quantitative data included in the gene samples. The interpretation information providing system  100  may determine genetic information from K samples using, for example, a relative log-expression (RLE) based data normalization technique or a trimmed mean of M value based normalization technique. 
     The interpretation information providing system  100  generates a plurality of gene modules by grouping RNAs and/or proteins included in the genetic information  3 , based on correlations thereamong (S 150 ). The interpretation information providing system  100  may search a correlation network of data included in the genetic representative information by de-novo, or may analyze correlations based on unsupervised machine learning. 
     The interpretation information providing system  100  determines information significantly enriched in each gene module, from functions defined in external databases, and annotates the determined information to each gene module (S 160 ). The external databases may include a biological database including gene feature information such as relationship information between biologically discovered genes and functions, pathways and interaction information, and the like, and medical databases utilized in medical fields such as biochemistry, medicine, pharmacy, and the like. The interpretation information providing system  100  may use gene set enrichment analysis (GSEA). The interpretation information providing system  100  may perform a significance test on association of functions extracted corresponding to each of the gene modules. The interpretation information providing system  100  may annotate significant enriched functions in each gene module as biological information, and may also annotate medical information related to the functions. 
     The interpretation information providing system  100  calculates a one-to-one relationship value (correlation value) between individual pathomics data included in the pathomics representative data and each gene module (S 170 ). As shown in  FIG. 3 , the interpretation information providing system  100  may calculate a one-to-one relationship value between individual pathomics data and each gene module. The interpretation information providing system  100  may shorten the value of each gene module in a designated manner and then calculate a relationship with individual pathomics data. 
     The interpretation information providing system  100  connects a gene module whose relationship value with individual pathomics data is equal to or greater than a threshold to a corresponding individual pathomics data (S 180 ). For example, the interpretation information providing system  100  may connect a gene module (color name of black) whose relationship values with the individual pathomics data CE_TIL_DEN and CS_TIL_DEN are greater than or equal to the threshold to CE_TIL_DEN and CS_TIL_DEN, respectively. Here, the gene module coded with the color name of black may be a gene module annotated with at least one function (for example, immune response and immune system process) and medical information related to the function. 
     The interpretation information providing system  100  provides the connected individual pathomics data and the gene module, and the annotated information to the gene module on the interface screen (S 190 ). The annotated information may be used as interpretation information for individual pathomics data. 
     The order of processes shown in  FIG. 10  may be changed according to a design, and the operations may be performed sequentially or in parallel. 
       FIG. 11  is a hardware configuration diagram of a computing device according to an embodiment. 
     Referring to  FIG. 11 , the interpretation information providing system  100  executes, in a computing device  300  operated by at least one processor, a program including instructions described to perform operations of the present disclosure. The program may be stored in a computer readable storage medium, and distributed as stored thereon. 
     The hardware of the computing device  300  may include at least one processor  310 , a memory  330 , a storage  350 , and a communication interface  370 , and may be connected via a bus. In addition, hardware such as an input device, an output device, and the like may be included. The computing device  300  may be equipped with a variety of software including an operating system executable the program. 
     The processor  310  is a device for controlling the operation of the computing device  300  and may be various types of processors for processing instructions included in a program. For example, the processor  310  may be a central processing unit (CPU), a micro processor unit (MPU), a micro controller unit (MCU), a graphic processing unit (GPU), and the like. The memory  330  loads the program such that the instructions described to perform the operations of the present disclosure are processed by the processor  310 . The memory  330  may be, for example, a read only memory (ROM), a random access memory (RAM), and the like. The storage  350  stores various data, programs, and the like required to perform the operations of the present disclosure. The communication interface  370  may be a wired/wireless communication module. 
     The above-described embodiments of the present disclosure are not only implemented through an apparatus and a method, but may also be implemented through a program for embodying functions corresponding to the configuration of the embodiments of the present disclosure or a recording medium where the program is recorded. 
     While the present disclosure has been illustrated and descried with reference to embodiments thereof, the right scope of the present disclosure is not limited thereto. Further, it will be understood by a person of ordinary skill in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the present disclosure as defined by the following claims.