bluuebunny/crossref_metadata_2025_split huggingface dataset:
Nomenclature-Based Data Retrieval without Prior Annotation:	Facilitating Biomedical Data Integration with Fast Doublet Matching
Authors: Berman Jules J. 
Published: January 2025

Assigning nomenclature codes to biomedical data is an arduous, expensive and error-prone task. Data records are coded to to provide a common representation of contained concepts, allowing facile retrieval of records via a standard terminology. In the medical field, cancer registrars, nurses, pathologists, and private clinicians all understand the importance of annotating medical records with vocabularies that codify the names of diseases, procedures, billing categories, etc. Molecular biologists need codified medical records so that they can discover or validate relationships between experimental data and clinical data. This paper introduces a new approach to retrieving data records without prior coding. The approach achieves the same result as a search over pre-coded records. It retrieves all records that contain any terms that are synonymous with a user's query-term. A recently described fast algorithm (the doublet method) permits quick iterative searches over every synonym for any term from any nomenclature occurring in a dataset of any size. As a demonstration, a 105+ Megabyte corpus of Pubmed abstracts was searched for medical terms. Query terms were matched against either of two vocabularies and expanded as an array of equivalent search items. A single search term may have over one hundred nomenclature synonyms, all of which were searched against the full database. Iterative searches of a list of concept-equivalent terms involves many more operations than a single search over pre-annotated concept codes. Nonetheless, the doublet method achieved fast query response times (0.05 seconds using Snomed and 5 seconds using the Developmental Lineage Classification of Neoplasms, on a computer with a 2.89 GHz processor). Pre-annotated datasets lose their value when the chosen vocabulary is replaced by a different vocabulary or by a different version of the same vocabulary. The doublet method can employ any version of any vocabulary with no pre-annotation. In many instances, the enormous effort and expense associated with data annotation can be eliminated by on-the-fly doublet matching. The algorithm for nomenclature-based database searches using the doublet method is described. Perl scripts for implementing the algorithm and testing execution speed are provided as open source documents available from the Association for Pathology Informatics (www.pathologyinformatics.org/informatics_r.htm).
Folded RNA from an Intron of One Gene Might Inhibit Expression of	a Counteracting Gene
Authors: Jeffries Clark , Jarstfer Michael , Perkins Diana O. 
Published: January 2025

Homeostatic maintenance of mRNA levels including prompt availability of mRNAs for translation in response to changing protein demands might be partly enabled by a system of combinatorial controls involving noncoding RNA blocking agents. This article proposes a specific version of that control mechanism, namely, a double-stranded RNA folding from transcription of an intron of one gene might and leading to an agent that inhibits mRNA of a counteracting gene. Thus transcription of the first gene would automatically repress translation of the second. On the basis of a bioinformatics search, we suggest a possible example, namely, that pro-apoptosis gene PAR4 might inhibit anti-apoptosis gene XIAP. Part of an intron from PAR4 folds to form a large, stable hairpin, and reverse complement of the hairpin stem (with ∼280 nucleotides) matches a sub-sequence of an exon in XIAP. This would be part of an efficient system to drive initiated apoptosis to its conclusion. Figuratively speaking, it replaces two control knobs with one. Since repeats, some with many thousands of copies, occur throughout the genome with complex distributions, care must be taken before asserting that the presence of any repeat in any gene is significant. Our apoptosis example involves repeats, so experimental verification is needed and planned. However, if it is found that the noncoding RNA by-product of one gene folds into a hairpin that is processed into an agent that inhibits a counteracting gene, then the same type of control unit might be found extensively among counteracting families of genes of many types.
AMIGOS: A Method for the Inspection of Genomic Organisation or	Structure and its Application to Characterise Conserved Gene Arrangements
Authors: Merkl Rainer 
Published: January 2025

In order to identify and to characterise gene clusters conserved in microbial genomes, the algorithm AMIGOS was developed. It is based on a categorisation of genes using a predefined set of gene functions (GFs). After the categorisation of all genes of a genome and based on their location on a replicon, distances between GFs were determined and stored in genome-specific matrices. These matrices were used to identify GF clusters like those strictly conserved in 13 archaeal, in 47 bacterial genomes and in the combination of the sets. Within the combined set of these 60 microbial genomes, there exist only two strictly conserved clusters harbouring two ribosomal genes each, namely those for L4, L23 and L22, L29. In order to characterise less strictly conserved GF clusters, content of genomes i.e. matrices were analysed pairwise. Resulting clusters were merged to (meta-) clusters if their content overlapped. A scoring system named cons $_{CL}$ was developed. It quantifies conservedness of cluster membership for individual GFs. For the genome of Escherichia coli it was shown that a grouping of cluster elements on cons $_{CL}$ values dissected the clusters into smaller sets. These sets were frequently overlapped by known transcriptional units (TUs). This finding justifies the usage of cons $_{CL}$ scores to predict TU membership of genes. In addition, cons $_{CL}$ values provide a sound basis for non-homologous gene annotation. Based on cons $_{CL}$ values, examples of conserved clusters containing annotated genes and single ones with unknown function are given.
GUIMACS – A Java Based Front End for GROMACS
Authors: Kota Pradeep 
Published: January 2025

Molecular dynamics simulations have gained importance due to their ability to provide valuable insights into understanding structure-function relationships of biological macromolecules. With increasing computational speeds there has been a substantial demand for optimization of simulation algorithms to obtain results even faster. With this on one hand, the need for ease of operation lies on the other. GUI front end programs are important appurtenances to ease the use of command line programs. Effective use of command line based programs requires basic knowledge of the UNIX shell and at least one of the UNIX based text editors, making it difficult for pure biologists to use them efficiently. GROMACS, a widely used suite of molecular dynamics simulation and analysis programs, is no exception to this. As a matter of fact, the increasing dependency of experimental procedures on computational methods for accentuating certain key experimental findings increases the need for interactivity in use of command-line based packages. GUIMACS is a Java based user-interactive front-end interface for the Linux version of GROMACS (version 3.3). GUIMACS runs as a standalone application with Multiple Document Interface (MDI) enabling its users to run and/or analyze multiple molecular dynamics simulations simultaneously.
Distinct Patterns in the Regulation and Evolution of Human Cancer	Genes
Authors: Furney Simon J. , Madden Stephen F. , Kisiel Tomasz A. , Higgins Desmond G. , Lopez-Bigas Nuria 
Published: January 2025

Understanding the mechanism of regulation of cancer genes and the constraints on their coding sequences is of fundamental importance in understanding the process of tumour development. Here we test the hypothesis that tumour suppressor genes and proto-oncogenes, due to their involvement in tumourigenesis, have distinct patterns of regulation and coding selective constraints compared to non-cancer genes. Indeed, we found significantly greater conservation in the promoter regions of proto-oncogenes, suggesting that these genes are more tightly regulated, i.e. they are more likely to contain a higher density of cis-regulatory elements. Furthermore, proto-oncogenes appear to be preferentially targeted by microRNAs and have longer 3' UTRs. In addition, proto-oncogene evolution appears to be highly constrained, compared to tumour suppressor genes and non-cancer genes. A number of these trends are confirmed in breast and colon cancer gene sets recently identified by mutational screening.
M@IA: A Modular Open-Source Application for Microarray Workflow	and Integrative Datamining
Authors: Le Béchec Antony , Zindy Pierre , Sierocinski Thomas , Petritis Dimitri , Bihouée Audrey , Le Meur Nolwenn , Léger Jean , Théret Nathalie 
Published: January 2025

Microarray technology is a widely used approach to gene expression analysis. Many tools for microarray management and data analysis have been developed, and recently new methods have been proposed for deciphering biological pathways by integrating microarray data with other data sources. However, to improve microarray analysis and provide meaningful gene interaction networks, integrated software solutions are still needed. Therefore, we developed M@IA, an environment for DNA microarray data analysis allowing gene network reconstruction. M@IA is a microarray integrated application which includes all of the steps of a microarray study, from MIAME-compliant raw data storage and processing gene expression analysis. Furthermore, M@IA allows automatic gene annotation based on ontology, metabolic/signalling pathways, protein interaction, miRNA and transcriptional factor associations, as well as integrative analysis of gene interaction networks. Statistical and graphical methods facilitate analysis, yielding new hypotheses on gene expression data. To illustrate our approach, we applied M@IA modules to microarray data taken from an experiment on liver tissue. We integrated differentially expressed genes with additional biological information, thus identifying new molecular interaction networks that are associated with fibrogenesis. M@IA is a new application for microarray management and data analysis, offering functional insights into microarray data by the combination of gene expression data and biological knowledge annotation based on interactive graphs. M@IA is an interactive multi-user interface based on a flexible modular architecture and it is freely available for academic users at http://maia.genouest.org.
Identification of Differentially Expressed Genes by Meta-Analysis	of Microarray Data on Breast Cancer
Authors: Kondrakhin Yury V. , Sharipov Ruslan N. , Kel Alexander E. , Kolpakov Fedor A. 
Published: January 2025

Albeit the great number of microarray data available on breast cancer, reliable identification of genes associated with breast cancer development remains a challenge. The aim of this work was to develop a novel method of meta-analysis for the identification of differentially expressed genes integrating results of several independent microarray experiments. We developed a statistical method for identification of up- and down-regulated genes to perform meta-analysis. The method takes advantage of hypergeometric and binomial distributions. Using our method we performed meta-analysis of five data sets from independent cDNA-microarray experiments on breast cancer. The meta-analysis revealed that 3.2% and 2.8% of the 24,726 analyzed genes are significantly (P-value < 0.01) down- and up-regulated, respectively. We also show that properly applied meta-analysis is a good tool for comparison of different breast cancer subtypes. Our meta-analysis showed that the expression of the majority of genes does not show significant differences in different subtypes of breast cancer. Here, we report the rationale, development and application of meta-analysis that enable us to identify biologically meaningful features of breast cancer. The algorithm we propose for the meta-analysis can reveal the features specific to the breast cancer subtypes and those common to breast cancer. The results allow us to revise the previously generated lists of genes associated with breast cancer and also identify most promising anticancer drug-target genes.
GO for Integration of Expression Data
Authors: Dotan-Cohen Dikla , Moonshine Dana , Natan Moshe , Shemer-Avni Yonat , Melkman Avraham A. 
Published: January 2025

The low reproducibility of differential expression of individual genes in microarray experiments has led to the suggestion that experiments be analyzed in terms of gene characteristics, such as GO categories or pathways, in order to enhance the robustness of the results. An implicit assumption of this approach is that the different experiments in effect randomly sample the genes participating in an active process. We argue that by the same rationale it is possible to perform this higher-level analysis on the aggregation of genes that are differentially-expressed in different expression-based studies, even if the experiments used different platforms. The aggregation increases the reliability of the results, it has the potential for uncovering signals that are liable to escape detection in the individual experiments, and it enables a more thorough mining of the ever more plentiful microarray data. We present here a proof-of-concept study of these ideas, using ten studies describing the changes in expression profiles of human host genes in response to infection by Retroviridae or Herpesviridae viral families. We supply a tool (accessible at www.cs.bgu.ac.il/∼waytogo) which enables the user to learn about genes and processes of interest in this study.
BTXpred: Prediction of Bacterial Toxins
Authors: Saha Sudipto , Raghava Gajendra P.S. 
Published: January 2025

This paper describes a method developed for predicting bacterial toxins from their amino acid sequences. All the modules, developed in this study, were trained and tested on a non-redundant dataset of 150 bacterial toxins that included 77 exotoxins and 73 endotoxins. Firstly, support vector machines (SVM) based modules were developed for predicting the bacterial toxins using amino acids and dipeptides composition and achieved an accuracy of 96.07% and 92.50%, respectively. Secondly, SVM based modules were developed for discriminating entotoxins and exotoxins, using amino acids and dipeptides composition and achieved an accuracy of 95.71% and 92.86%, respectively. In addition, modules have been developed for classifying the exotoxins (e.g. activate adenylate cyclase, activate guanylate cyclase, neurotoxins) using hidden Markov models (HMM), PSI-BLAST and a combination of the two and achieved overall accuracy of 95.75%, 97.87% and 100%, respectively. Based on the above study, a web server called 'BTXpred' has been developed, which is available at http://www.imtech.res.in/raghava/btxpred/. Supplementary information is available at http://www.imtech.res.in/raghava/btxpred/supplementary.html.
The CAI Analyser Package: Inferring Gene Expressivity from Raw	Genomic Data
Authors: Ramazzotti Matteo , Brilli Matteo , Fani Renato , Manao Giampaolo , Degl'Innocenti Donatella 
Published: January 2025

The Codon Adaptation Index (CAI) was introduced by Sharp and Li in 1987 to quantify codon usage similarities between a coding sequence and a set of reference sequences. When synonymous codons for a given amino acid exist, highly expressed genes seem to prefer some of them, according to tRNA abundance and thermodynamic issues. Some authors have described CAI-based methods to derive expressivity measures for all genes in a genome, in a computational framework. Here we present the CAIAP (CAI Analyser Package), a platform independent package of computer programs allowing the calculation of the CAI and a deep study of gene expressivity from raw gene sequences. Our approach implements and optimizes a procedure to derive the reference sequences from whole genomes and use their codon usage for CAI estimation. Moreover, a set of analysis tools are provided to perform statistical analyses and therefore to give robustness to results. Objective: Our efforts were aimed to produce an easy-to-use and fully automatic set of programs specifically designed for the analysis of gene expressivity and inter-species comparisons on a great number of genomes. Moreover, the output integrates information coming from functional annotations of genes. We are maintaining a web server storing our analyses for hundreds of genomes, allowing intergenomic comparison of data thanks to dedicated search engines. The CAIAP server is hosted at www4.unifi.it/scibio/bioinfo/caiap/html. The programs (maintained as Perl scripts) are also available for download at the same location.
Cell Interaction Knowledgebase: An Online Database for Innate	Immune Cells, Cytokines and Chemokines
Authors: Chen Shu , Chinnaswamy Arunkumar , Biswas Subhra K. , Goryachev Andrew B. , Yap Choon-Kong , Lam Koi-Yau , Tay Cheng-Pei , Low Hong-Sang , Pokhilko Alexandra , Da-Jun Toh , Mishra Santosh K. 
Published: January 2025

The innate immune system is fundamental to the recognition of pathogens, triggering of immune-inflammatory response and host defense. Recent advance in this area has resulted in enormous amount of data, which are stored across different databases. Integrating relevant information from these different data sources is difficult because of their heterogeneous nature and dispersed physical location. We present here a single portal system, Cell Interaction Knowledgebase, with focus on the innate immunity. In particular, the knowledgebase houses comprehensive information on innate immune cells and cytokines/chemokines which are the principal mediators of communication among the immune cells. Currently the knowledgebase consists of extensive information on 2 major innate immune cell types (Macrophages and Dendritic cells) and 7 6 cytokines/chemokines for both human and mouse. In addition, intra-cellular molecular interactions and inter-cellular interactions involved in the innate immunity are curated and presented in an interactive and dynamic manner by animated pathways and query-driven cell-interaction map respectively. This is one of the first databases that houses extensive phenotypic, signaling, genomic, proteomic and knockout data on both the innate immune cells and their attendant cytokines/chemokines, and is aimed to evolve as a one-stop-shop for immunologists. The first version of database is available at http://cell-interaction.bii.a-star.edu.sg/.
The Use of Multiple Hierarchically Independent Gene Ontology Terms	in Gene Function Prediction and Genome Annotation
Authors: Kourmpetis Yiannis A.I. , van der Burgt Ate , Bink Marco C.A.M. , ter Braak Cajo J.F. , van Ham Roeland C.H.J. 
Published: January 2025

The Gene Ontology (GO) is a widely used controlled vocabulary for the description of gene function. In this study we quantify the usage of multiple and hierarchically independent GO terms in the curated genome annotations of seven well-studied species. In most genomes, significant proportions (6–60%) of genes have been annotated with multiple and hierarchically independent terms. This may be necessary to attain adequate specificity of description. One noticeable exception is Arabidopsis thaliana, in which genes are much less frequently annotated with multiple terms (6–14%). In contrast, an analysis of the occurrence of InterPro hits in the proteomes of the seven species, followed by a mapping of the hits to GO terms, did not reveal an aberrant pattern for the A. thaliana genome. This study shows the widespread usage of multiple hierarchically independent GO terms in the functional annotation of genes. By consequence, probabilistic methods that aim to predict gene function automatically through integration of diverse genomic datasets, and that employ the GO, must be able to predict such multiple terms. We attribute the low frequency with which multiple GO terms are used in Arabidopsis to deviating practices in the genome annotation and curation process between communities of annotators. This may bias genome-scale comparisons of gene function between different species. GO term assignment should therefore be performed according to strictly similar rules and standards.
Improving Leadership Decision-Making (LDM) through Knowledge Management (KM) in Public Sector (PS) of Pakistan
Authors: Salam Muhammad Abdus , Haq Naveed ul 
Published: January 2025

The current study aimed to identify the existing leadership decision-making (LDM) in the public sector (PS) of Pakistan along with its role, barriers, constraints, and issues. Thereafter, the study attempted to examine the application of knowledge management (KM) principles and techniques for the augmentation of LDM to enhance the overall efficacy of PS. The study was qualitative in nature and comprised rigorous literature review and examination of archival records of PS in Pakistan. Additionally, it also analyzed the personal insights and reflections from the PS of Pakistan. The findings revealed that the field is in embryonic stage and requires extensive research in qualitative as well as quantitative domain. There is even a lack of basic awareness pertaining to KM and its subfields encompassing tacit knowledge, explicit knowledge, and phronesis. However, the growth of private sector and the role of key decisions taken by the leadership in shaping their sustainable success is a prime lesson to be learnt by the PS in terms of adoption of KM for better decision-making (DM). Moreover, the study also focused on the development of a model by modifying an existing DM organizational model presented in 2017. However, generalizability could be an issue, since this study was conducted based on the personal reflections of the researcher. Additionally, empirical validation, quantitative, longitudinal, cross-sectional, and case study researches may b conducted to evaluate the implications of the study. PS is considered as a non-profit organizational setup. There is a wide gap between public and private sector due to the difference in the role played by both. However, a number of practices can be adopted interchangeably to improve and enhance the existing capabilities which in case of PS include governance, compliance, and citizen satisfaction. The current study provided theoretical insights into how KM models, themes, and practices can be applied for the improvement of DM in the PS. The model presented was novel and an addition to the existing body of knowledge.
MUSCULOSKELETAL DISORDER ASSOCIATED WITH DEVICE USAGE AT THE TEACHER PROFESSION
Authors: Septadina Indri Seta , Wardiansah  , Fatimah Nyimas , Fitri Agita Diora 
Published: January 2025

. One of the most common diseases in workers is musculoskeletal disorders. where the prevalence of musculoskeletal disorders in teachers is around 39% to 95%. Most teachers experience chronic musculoskeletal disorders and subjective complaints, one of which is pain in the upper limbs. Some studies show the relationship between gadgets and user posture can cause discomfort and fatigue in the hands, elbows, and shoulders. This article discuss about the use of gadgets in an unergonomic position often results in musculoskeletal complaints. Static loading by holding handheld devices or gadgets for long periods, often with unergonomic postures and excessive use of muscles in the upper limbs, is likely to contribute to myofascial pain syndrome in the muscles of the hands and arms, resulting in pain in the upper limbs.