The clinical significance of hematogenous and lymphatic metastasis in ovarian cancer has been increasingly addressed, as it plays an imperative role in the formation of both intraperitoneal and distant metastases. Our objective is to identify the key molecules and biological processes potentially related to this relatively novel metastatic route in serous ovarian cancer.Since lymphovascular space invasion (LVSI) is considered as the first step of hematogenous and lymphatic dissemination, we developed a gene signature mainly based on the transcriptome profiles with available information on LVSI status in the Cancer Genome Atlas (TCGA) dataset. We then explored the underlying biological rationale and prognostic value of the identified gene signature using multiple public databases.We observe that primary tumors with increased risk of hematogenous and lymphatic metastasis highly express a panel of genes, namely POSTN, LUM, THBS2, COL3A1, COL5A1, COL5A2, FAP1 and FBN1. The identified geneset is characterized by enhanced deposition of extracellular matrix and extensive stromal activation. Mechanistically, both the recruitment and the activation of stromal cells, especially fibroblasts, are closely associated with lymphovascular metastasis. Survival analysis further reveals that the elevated expression of the identified genes correlates to cancer progression and poor prognosis in patients with serous ovarian cancer.Our findings indicate that tumor stroma supports the hematogenous and lymphatic spread of ovarian cancer, increasing tumor invasiveness and ultimately resulting in worse survival. Thus stroma-targeted therapies may improve the clinical outcomes in combination with cytoreductive surgery and chemotherapy. Ovarian cancer is the most lethal gynecological malignancy, and a fair number of patients are diagnosed in advanced stage with extensive intraperitoneal spread and distant metastases . ExploraIt has long been assumed that direct shedding of ovarian cancer cells from the primary site into the intraperitoneal cavity is the most predominant route for the formation of metastatic diseases . HematogLymphovascular space invasion (LVSI), describing the presence of tumor cells within the lumen of lymphatic or vascular capillaries of the primary tumor, is demonstrated to associate with worse clinical outcomes in patients with ovarian cancer , 7. As thttp://www.ncbi.nlm.nih.gov/geo) and preprocessed by the robust multi-array average algorithm (RMA). Clinical and pathological characteristics of the cohorts of patients analyzed in this study was listed in Additional file Microarray transcriptome profiles from TCGA, GSE26712, GSE9891, GSE49997 and the corresponding clinical metadata were downloaded from the curatedOvarianData database ) in patients with late-stage diseases undergoing optimal cytoreduction  and possess multiple tumor-promoting functions, facilitating tumor invasion and metastasis through direct and indirect crosstalk with tumor cells as well as with other non-tumor components like immune cells and endothelial cells .Firstly, the co-evolution of cancer cells and CAFs facilitate tumor growth and spread via multiple interactions. CAFs produce autocrine and paracrine cytokines, chemokines and growth factors like CXCL1, CCL5, HB-EGF and TGF-α, subsequently promoting cancer cell invasiveness through upregulation of matrix metalloproteinases (MMPs) and induction of EMT. CAFs can additionally reprogram ovarian cancer cell metabolism via producing metabolites or altering the key enzymatic activities . In turnBesides, angiogenesis and inflammatory response are involved in the tumor-stroma crosstalk, which was also revealed in the above enrichment analysis. Pathological angiogenesis, supplying adequate nutrients and oxygen to tumor cells, often indicates the metastatic potential in tumors. CAFs secretes key pro-angiogenic factor VEGF-A as a result of HOXA9 upregulation from ovarian cancer cells, promoting proliferation and invasiveness of endothelial cells so as to trigger angiogenesis . Many ofReferred to as “wounds that never heal”, cancer and chronic inflammation are closely correlated. CAFs overexpress pro-inflammatory chemokines and cytokines such as IL-6, COX-2, and CXCL1, which mediate tumor-related inflammation and induce carcinogenesis . These CConsidering the essential role of tumor stroma in promoting cancer progression, strategies targeting stromal cells exert great potential in improving clinical outcomes of ovarian cancer patients. Notably, low resistance rate is a distinct advantage of anti-stromal therapies, largely relying on the genetic stability of stromal cells. Among the genes identified in the present study, the safety of FAP-antibody sibrotuzumab has been validated in phase I trials in patients with tumors highly expressing FAP . AlthougIn addition, TGF-β-targeted therapies are currently investigated as a promising approach to impede cancer progression, since TGF-β has been demonstrated as the key driver of fibroblast activation and CAF formation in different cancer types. In line with the available researches , 40, somCurrently, there are over 50 clinical trials evaluating TGF-β-targeted therapies in cancers . For patOf note, some genes we explored overlap with previously identified gene signatures associated with cancer progression and poor survival in various solid tumors including ovarian , 43, breIn this manuscript, we identified a panel of genes closely correlated with hematogenous and lymphatic metastasis of serous ovarian cancer. The upregulation of this gene signature, predominantly expressed by the increased infiltration of reactive stroma, is further confirmed to associate with tumor invasiveness and poor survival. Utilizing multiple transcriptome profiles available in the public database, we explored the potential biological rationale underlying this relatively novel metastatic route of ovarian cancer, highlighting the imperative role of tumor stroma in the development of ovarian cancer metastasis.Additional file 1: Table S1. Clinical and pathological characteristics of the cohorts of patients analyzed in the manuscript. Table S2. Flowchart of this study. Table S3. Eight genes common to both LVSI- and metastasis-related DEGs were listed. Fold changes and adjusted P values were generated by limma package. Table S4. Results of purity-corrected correction analysis from TIMER database showed a significant but weak correlation between the expression levels of the identified genes and the infiltration of immune cells in ovarian cancer samples. Table S5. The expression of the LMGS in the ovarian cancer CCLE cell lines were ranked, with the corresponding EMT phenotypes annotated based on two public sourcesAdditional file 2: Figure. S1. (a) Paired t-test revealed that all eight genes were significantly elevated in omental metastases compared with the corresponding primary ovarian tumors in the dataset GSE30587. (b) Four genes  of the LMGS were remarkably elevated, while COL5A1 was significantly down-regulated in SKOV3-OM3 , compared to SKOV3ip1 intraperitoneal injected to the host mice (representing the primary tumors) in the dataset GSE52999. (c) Genes of the LMGS were likely to form a biologically functional network based on PPI analysis. Primary OV: primary ovarian cancer samples, Metastatic OM: omental metastases of ovarian cancer. * P < 0.05, ** P < 0.01, *** P < 0.001Additional file 3: Figure. S2. All eight genes of the LMGS were highly enriched in the C1 molecular subtype of (a) the Tothill dataset and (b) in the TCGA mesenchymal subtype. (c) The expression levels of individual gene of the LMGS were significantly elevated in tumor stroma compared with the epithelial components in dataset GSE38666. A similar tendency was observed in paired samples in (d) dataset GSE115635, as well as in stromal components compared with normal ovarian stroma in (e) dataset GSE40595.Additional file 4: Figure. S3. The significant and negative correlation between the activation of the LMGS and tumor purity was validated in (a) GSE9891 and (b) GSE26712. (c-e) The positive correlation between the expression of the LMGS and immune cell infiltration was significant but relatively weak. The activation of the LMGS was positively correlated with mesenchymal infiltration in serous ovarian cancer samples from (f) GSE9891 and (g) GSE26712. The infiltration of immunocytes was similar between the primary ovarian cancer samples with LVSI-positive status versus LVSI-negative ones. A similar trend was observed in omental metastases compared with primary lesions in (h) dataset GSE2109 and was validated in (i) paired samples from dataset GSE30587Additional file 5: Figure. S4. Genes of the LMGS were remarkably elevated in (a-c) patients undergoing suboptimal cytoreduction and (d-f) those with late-stage serous ovarian cancer


The opioid crisis can be seen as a double crisis or a dual epidemic: the crisis of nonmedical use and the crisis of uncontrolled pain.It is important to ensure both crisis are adequately addressed.The evidence base of clinical guidelines to ensure safe and appropriate opioid prescribing as well as compliance with  these  guidelines  can  be  further  improved.There is a striking absence on research related to the lack of equal access to medically justified opioids. Additionally, there is a lack of evidence on unintended consequences of policy and regulatory actions.Strategies to battle nonmedical use of opioids should not go at the expense of access for patients in legitimate medical need. Close monitoring is needed to minimize unintended consequences.1Pharmacoepidemiology and Drug Safety, several research papers address the safe and appropriate use of opioids. This is important, given the critical situation on opioid use in the United States (US). According to preliminary data from the Centers for Disease Control and Prevention (CDC), more than 72 000 people in the United States died because of drug overdose in 2017 with over two‐thirds involving opioids.In this issue of 2We acknowledge that abuse and diversion of opioids constitute a serious threat to public health. But it is also important to recognize that there is another side of the coin: opioids are an indispensable pharmaceutical treatment option for patients in pain, and many patients in medical need have inadequate access. Data from the International Narcotics Control Board (INCB) show that 95.7% of the global consumption of opioid analgesics in 2011 to 2013 took place in regions representing only 15% of the global population.3In recent years, there has been a large increase in the number of scientific studies reporting on the nonmedical use of opioids. Despite this scientific focus, there is still a lack of evidence‐based guidance on the safe prescribing of opioid medicines and on the pathways from opioid dependence to opioid overdosing. Several studies have identified medication‐related and patient‐related factors associated with the risk of opioid overdose. Medication‐related factors include the use of long‐acting or extended release formulations , combined use with benzodiazepines, high daily doses, and long‐term opioid use., Ranapurwala et al revealed several internal and external validity concerns in the opioid safety studies.Prescribing guidelines are important to guide clinicians in the safe and appropriate prescribing of opioids. However, the CDC guidelines are also criticized for making some recommendations that are not supported by current scientific evidence, which is also acknowledged by the guidelines themselves.4Pharmacoepidemiology and Drug Safety, two papers have assessed compliance with CDC recommendations.Aside from the level of evidence, it is useful to know to what extent these guidelines are followed in practice. In the current issue of Compliance with clinical guidelines is an essential prerequisite for the functioning of health systems. But clinical guidelines are typically based on the average patient; there may be certain patients with individual circumstances that justify deviating from guidelines. For example, there may be patients that require treatment with an extended release formulation, or with a higher dose than 90 MME at onset of their treatment. This also applies to patients with a history or high susceptibility of opioid dependence; this group represents a particularly disadvantaged and challenging population. Providing these patients with opioids in a balanced fashion remains critical. Since this is a high‐risk population, close clinical monitoring, management of abuse risk, and adequate access to opioid dependence treatment are crucial when opioid analgesics are justifiably used for pain management.5Although the focus on appropriate prescribing and dispensing is understandable given the current opioid abuse and misuse crisis, there is a striking absence on research related to the other crisis, ie, the lack of equal access to medically justified opioids. A review of 46 articles published between 2007 and 2013 showed that 31.8% of the patients with cancer did not receive adequate pain relief.Societal attitudes regarding the medical use of opioid analgesics may have changed because of the opioid epidemic. In discussions addressing this crisis, people may not always distinguish between overdose, misuse or illegal diversion of prescribed opioids, and use of illicit opioids. This confusion may result in a disproportionate generalized fear of opioids, limiting access for patients in medical need. Some experts believe that most patients with opioid dependence are recreational drug users who become dependent, rather than patients with pain becoming patients with opioid dependence.Although it is beyond any doubt that nonmedical use and diversion of opioids should be battled, this should not go at the expense of balanced strategies to ensure access to medicines that are legitimately on the market for patients in need of essential pain relief. The issue is how to monitor and minimize potential unintended consequences for these patients. The Access to Opioid Medication in Europe (ATOME) project—aimed at the increase of access to opioid medicines in 12 countries with statistical evidence of low opioid consumption‐signaled clearly the importance of sustained investments in public health and education, and improved legal and regulatory systems to ensure safe and appropriate treatment of pain.www.tipharma.nl), is accepted under the condition that no company‐specific product or company related study is conducted. The Centre has received unrestricted research funding from public sources, eg, Netherlands Organization for Health Research and Development (ZonMW), the Dutch Healthcare Insurance Board (ZIN), EU 7th Framework Program (FP7), the Dutch Medicines Evaluation Board (MEB), and the Dutch Ministry of Health, Welfare and Sport.The Division of Pharmacoepidemiology and Clinical Pharmacology of Utrecht University is designated as a WHO Collaborating Centre for Pharmaceutical and Regulation. The WHO Collaborating Centre for Pharmaceutical Policy and Regulation receives no direct funding or donations from private parties, including pharma industry. Research funding from public‐private partnerships, eg, IMI, TI Pharma (


As a result, our model performed well in the global and local cross-validations, which indicated that IDLDA had a great performance in predicting novel associations. Case studies of colon cancer, breast cancer, and gastric cancer were also implemented, all lncRNAs which ranked top 10 in both databases were verified by databases and related literature. The results showed that IDLDA might play a key role in biomedical research.It has been demonstrated that long non-coding RNAs (lncRNAs) play important roles in a variety of biological processes associated with human diseases. However, the identification of lncRNA–disease associations by experimental methods is time-consuming and labor-intensive. Computational methods provide an effective strategy to predict more potential lncRNA–disease associations to some degree. Based on the hypothesis that phenotypically similar diseases are often associated with functionally similar lncRNAs and  Non-coding RNA (ncRNA) is a kind of RNA molecule that is not translated into protein . In decaRecently, exploiting potential lncRNA–disease associations have become a growing significant research area. Many associations between lncRNA and human diseases have been identified by medical experiments, but which is costly and time-consuming. Predicting potential associations by the mathematical method and computational inference for experimental verification is a quite certain well-selected alternative .vice versa. IDLDA achieved reliable predictions with global and local cross-validations and it obtained higher AUROC than some previously proposed methods. Our results showed that the predicted top 10 lncRNAs in both databases were confirmed by databases and literature, and there were only 2, 2, and 1 lncRNAs which ranked top 50 by IDLDA in both databases that were not confirmed. All these results demonstrated the effectiveness and value of IDLDA in identifying potential lncRNA–disease associations. Data and code are freely available for research purposes only, you can email the author for it.In this paper, we developed an improved diffusion model for predicting lncRNA–disease associations (IDLDA) based on the hypothesis that phenotypically similar diseases are often associated with functionally similar lncRNAs and LncRNADisease  and Lnc2G as follows. V=L∪D is the vertex set, where L is the lncRNA set { l1,l2,…,lNl }, D is the disease set { d1,d2,…,dNd }, and denote the edge set E={ eij:di∈D,lj∈L }. Nd and Nl represent the number of diseases and the number of lncRNAs, respectively. Here, the lncRNA–disease association can be represented by an adjacency matrix A={aij}Nd×Nl, where aij=1 if disease di and lncRNA lj have experimentally validated relation in the databases, while the unknown associations are set to 0 indicating that they will be ranked.We constructed lncRNA–disease associations as a bipartite graph dj in the MeSH database, we constructed a directed acyclic graph DAG(dj) based on the MeSH descriptors of Category C downloaded from the National Library of Medicine. For example, V(DAG(dj)) indicated the vertex set including vertex dj and its ancestor vertices, and E(DAG(dj)) was the edge set of corresponding direct links from a parent vertex to a child vertex, which represented the relationship between different diseases.For every disease term dj, in the DAG(dj), the contribution of each disease semantic term Cdj(di) of disease di was defined as follows  ∩ DAG(dj) should have a higher semantic similarity. Thus, the semantic score of disease dj was acquired by adding up all the contributions from ancestor diseases and disease dj itself. Define the semantic score (C) of disease dj as follows:According to this way to measure disease semantic similarity, we thought that two diseases SS) between disease di and disease dj can be written as  between disease di and disease dj.Based on the basic assumption that two lncRNAs with more functional similarity prefer to be more related to similar diseases and ce versa , we coulce versa . Then weIP(di) was the i-th column of matrix A. The parameter γd was a parameter for adjusting the bandwidth of the kernel, which should be updated by using a new bandwidth parameter where us study , γd’waγd could be defined as follows:Thus, DS) between disease di and disease dj as follows:Define the disease ensemble similarity  and D(lj) be the set of diseases related to lncRNA li and lncRNA lj, respectively. Define similarity score S between D(li) and D(lj) as follows:Let vice versa  | and | D(lj) | were the numbers of diseases associated with lncRNA li and lncRNA lj, respectively.where | li and lncRNA lj was defined as follows  between lncRNA li and lncRNA lj as follows:Define the lncRNA ensemble similarity  of the dj vertex was shown as follows:First of all, we selected one disease dj vertex returned back to L by LA and DA. Then the final comprehensive index (resources) of the li vertex as shown below:Each disease scattered the received resources to its associated lncRNAs, the resources located on the LA and DA. Therefore, for a given disease Du, we could obtain the comprehensive index IDLDA-score of every lncRNA. Accordingly, we got the predicted ranks of all lncRNAs for every disease. This predicted result can be represented by a rank matrix R={rij}Nd×Nl, where rij indicated the relevance score between disease di and lncRNA lj. The larger the value of rij, the more likely disease di and lncRNA lj are to be related. Thus, IDLDA can predict not only new disease-related lncRNAs but new lncRNA-related diseases. The flow chart of IDLDA is shown in Here the parameters α, β were used to balance the contribution between In this section, we first analyzed some properties of the lncRNA–disease association network. Next, we used global and local cross-validations and performed enrichment analysis to evaluate the performance of IDLDA. Then, we conducted case studies to verify the efficiency of IDLDA in discovering some potential disease-related lncRNAs.We analyzed the lncRNA–disease association network’s characteristics to obtain a whole view of it Table 1.A receiver operating characteristic (ROC) curve is a graphical plot that shows the diagnostic ability of the binary classifier system because its recognition thresholds are different . AUROC /(MN) , where mCase studies were implemented to examine the capability of IDLDA in discovering potential lncRNA–disease associations. For some special diseases, we ranked those candidate lncRNAs based on their corresponding IDLDA-scores. Case studies included three common human diseases . Prediction results were verified based on not only the recent updates in the Lnc2Cancer and LncRNADisease but recently published experimental literature. Then we observed the number of the verified lncRNAs in the top 10 and 50 predictions in both databases, all the ranking results have been listed in Colon cancer is one of the most common malignant tumors in the world , killingBreast cancer is the second leading cause of cancer deaths in women, accounting for 22% of all cancer deaths in women . Some reGastric cancer is the second major reason for cancer-related death in the world . A myriaAccording to previous literature, lncRNAs are associated with a mass of diseases. With the emergence of many biological data about lncRNA, it is urgent to design a powerful and effective computing method to predict the underlying disease-related lncRNAs. In this paper, disease semantic similarity, lncRNA functional similarity, disease/lncRNA Gaussian kernel similarity, and lncRNA–disease associations were integrated on a large scale. We developed a computational model named IDLDA, which based on the diffusion model to predict potential lncRNA–disease associations. IDLDA achieved higher AUROC than other methods in the combined dataset. Meanwhile, local cross-validation, enrichment analysis could also show the reliability of the model. Moreover, case studies of colon cancer, breast cancer, and gastric cancer were also implemented, all lncRNAs which ranked top 10 in both databases were verified, only 2, 2, and 1 lncRNAs which ranked top 50 in both databases were not confirmed by databases and related literature. What is more, the results of local cross-validation showed IDLDA can predict not only new disease-related lncRNAs but new lncRNA-related diseases.Here are the reasons why IDLDA performs better than some aforementioned methods. Firstly, the lncRNA ensemble similarity and disease ensemble similarity can make full use of the information about known lncRNA–disease associations by integrating lncRNA functional similarity, disease semantic similarity, and the Gaussian kernel similarity. Secondly, both disease ensemble similarity and lncRNA ensemble similarity are used in the diffusion process, IDLDA could predict not only new lncRNAs but also new diseases, overcoming some limitations of previous methods. Thirdly, IDLDA as a semi-supervised method is superior to the supervised methods when the data is incomplete. In particular, semi-supervised method could be implemented without any negative lncRNA–disease associations, which are closer to reality. In short, IDLDA will be an important and powerful bioinformatics tool in biomedical research of the lncRNA–disease association prediction, and even disease treatment.Although IDLDA is effective, this work has several limitations. Firstly, IDLDA contains two parameters, and finding suitable parameters for different datasets is a challenging task. Additionally, some specific lncRNAs are not associated with certain diseases. If this kind of data can be added to the model in the future, it will certainly be helpful to improve the predictive ability. Successfully established models in the other computational fields would inspire the development of lncRNA–disease association prediction. Perhaps we can improve the predictive performance of IDLDA by integrating more information, such as lncRNA–miRNA information  and disehttp://www.cuilab.cn/lncrnadisease , http://www.bio-bigdata.net/lnc2cancer. Publicly available datasets were analyzed in this study. This data can be found here: QW conceived the project, developed the prediction method, designed the experiments, implemented the experiments, analyzed the result, and wrote the paper. GY analyzed the result and revised the paper.GY was supported by the National Natural Science Foundation of China under Grant No. 11631014.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Long non-coding RNAs (lncRNAs) play a crucial role in the pathogenesis and development of complex diseases. Predicting potential lncRNA–disease associations can improve our understanding of the molecular mechanisms of human diseases and help identify biomarkers for disease diagnosis, treatment, and prevention. Previous research methods have mostly integrated the similarity and association information of lncRNAs and diseases, without considering the topological structure information among these nodes, which is important for predicting lncRNA–disease associations. We propose a method based on information flow propagation and convolutional neural networks, called LDAPred, to predict disease-related lncRNAs. LDAPred not only integrates the similarities, associations, and interactions among lncRNAs, diseases, and miRNAs, but also exploits the topological structures formed by them. In this study, we construct a dual convolutional neural network-based framework that comprises the left and right sides. The embedding layer on the left side is established by utilizing lncRNA, miRNA, and disease-related biological premises. On the right side of the frame, multiple types of similarity, association, and interaction relationships among lncRNAs, diseases, and miRNAs are calculated based on information flow propagation on the bi-layer networks, such as the lncRNA–disease network. They contain the network topological structure and they are learned by the right side of the framework. The experimental results based on five-fold cross-validation indicate that LDAPred performs better than several state-of-the-art methods. Case studies on breast cancer, colon cancer, and osteosarcoma further demonstrate LDAPred’s ability to discover potential lncRNA–disease associations. Many studies have indicated that protein-coding genes only account for ~2% of the human genome, whereas non-coding protein sequences account for ~98% ,2,3,4,5.The calculation methods employed for predicting potential lncRNA–disease associations can be broadly divided into three categories. The first method uses the biological information of lncRNA to identify lncRNA–disease associations, such as the expression profile, tissue specificity, and genome location. Li et al.  predicteThe second method uses machine learning models to predict the potential associations. Chen et al.  proposedThe third method establishes heterogeneous networks, based on which lncRNA–disease associations can be predicted. Zhang et al.  construcIn this study, we propose a method, called LDAPred, based on information flow propagation and a convolutional neural network, to predict potential lncRNA–disease associations. LDAPred utilises the similarities, associations, and interactions among lncRNAs, miRNAs, and diseases. On the left side of the network, the original feature matrix of lncRNA–disease node pairs was constructed from the biological perspective. On the right side, according to the information flow propagation of the bi-layer network formed by lncRNA, miRNA, and disease, the possibility of interconnections between them was calculated, and the characteristic matrix was formed. Dual convolution was used to learn deeper features and make association predictions. Combined with five-fold cross-validation experiments, the results indicate that LDAPred is better than several existing methods for the prediction of candidate lncRNAs. Moreover, the results of the case study on breast cancer, colon cancer, and osteosarcoma also indicate that LDAPred has a strong ability to identify potential disease lncRNAs.To achieve the best prediction result, we repeatedly verified the results by conducting experiments. Finally, the filter used in the convolutional layer and the pooling layer in the dual channel system was set to the dimension of To evaluate the performance of the prediction model, we used five-fold cross-validation. First, the known 2687 lncRNA–disease associations were divided into five groups, four of which were used as the training set and one as the test set. Second, we deleted the association in the test set when calculating the similarity of lncRNAs. We regarded those with lncRNA-related diseases in the test set as positive cases and those without any association as negative cases.TP is the number of positive samples that are considered positive, and TN is the number of counterexamples that are considered counterexamples. FN is the number of positive examples that are considered counterexamples, and FP is the number of counterexamples that are considered positive examples. Finally, the average of all disease AUCs was taken to represent the performance of the predictive model. The higher the value, the higher the global performance of the model.After using our prediction model to evaluate the associated scores of the test samples, the scores of the samples were ranked in descending order. The higher the ranking of the positive examples, the better the prediction performance of the model. We measured the global performance of our prediction model by drawing the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC). The true positive rate (TPR) and false positive rate (FPR) can be defined as follows:Because the lncRNA–disease sample has a number of associated positive examples that are smaller than the unrelated or unrecognized counterexamples, there is a serious imbalance ratio. Therefore, we also used the precision–recall (PR) curve to measure the overall performance of the model. The larger the area under the PR curve (AUPR), the better the prediction performance. The precision and recall can be calculated as follows:k  samples, i.e., the ratio of the positive samples in the first k samples to all the predicted positive samples, as another performance index.Biological experiments are costly and time-consuming and limited by equipment precision and human error; thus, biologists choose to predict the top lncRNA to verify the disease associated with it. Therefore, we also calculated the recall rate of the first To reveal the advantages of considering network topology information in lncRNA–disease association prediction modeling and demonstrate the strong performance of our model, we selected four latest lncRNA–disease association prediction methods, namely SIMCLDA , Ping’s As shown in As shown in p-value of 0.05. In addition, to assess whether the AUC performance of LDAPred for all 405 diseases is better than those of the other four methods, we performed a paired Wilcoxon test. The statistical results are shown in k lncRNAs, the greater the number of correctly identified lncRNAs that are related to the disease. k samples of all 405 diseases. LDAPred is superior to the other methods at different k values, accounting for 86.4% in the top 30, 92.8% in the top 60, 95.1% in the top 90, and 96.3% in the top 120. The recall rate of Ping’s method is very close to that of LDAP. The former accounts for 68.9%, 81.2%, 87.5%, and 92.7% among the top 30, 60, 90, and 120, whereas the latter accounts for 68.5%, 81.7%, 88.0%, and 93.3%, respectively. SIMCLDA accounts for 49.3% in the top 30, 63.0% in the top 60, 74.1% in the top 90, and 80.3% in the top 120, exhibiting lower values than Ping’s method and LDAP. Compared to the four methods, MFLDA always shows the worst performance, accounting for 42.0%, 53.9%, 60.9%, and 65.5%, respectively.The higher the recall rate of the top To further demonstrate the LDAPred’s ability to detect disease-related lncRNAs, we used two separate databases (Lnc2Cancer and lncRNADisease) and related literature to validate candidate genes for breast cancer, colon cancer, and osteosarcoma. The top 15 candidate lncRNAs associated with these cancers were analysed separately .Lnc2Cancer is an experimentally supported lncRNA manual management database for various human cancers . It contThe remaining three candidates reported in previous studies are marked as the “literature” in To predict the relationships between lncRNAs and diseases, we needed to integrate the attributes and characteristics of each node of the lncRNAs, miRNAs, and diseases. Therefore, we downloaded 2687 lncRNA–disease associations from the LncRNADisease  and Lnc2A disease can be expressed as a directed acyclic graph (DAG), which can be obtained from Medical Subject Headings (MeSH), and it includes all relevant annotated items of the disease. Studies have shown that the more common the DAG of two diseases, the more similar the two diseases. Wang et al.  measuredThe more similar the functions of two lncRNAs, the more similar the related diseases. Therefore, we calculated the similarity of two lncRNAs by calculating the similarity of the two lncRNA-associated diseases. For example, lncRNA n et al. , the simSimilar to the lncRNA similarity calculation, the miRNA similarity was calculated based on the associated diseases. We used the matrix A, B, C, In this study, heterogeneous data resources were synthesized and the interaction matrix was established: the lncRNA–disease association matrix We constructed a dual convolutional neural network (CNN) predictive model to predict the lncRNA–disease associations. The left side uses the original information of the lncRNA L, and the association vector A, and combined them together. Second, if C, vector C, and We utilized lncRNA d+nl+nm) .Inspired by Chen et al. , we consL represents the original information between lncRNA nodes; i.e., the one-hop similarity information. In a network comprising lncRNAs, Similarly, In a network comprising lncRNAs and diseases, A represents the one-hop information between lncRNA and disease node pairs, and Similarly, the association information between the disease and miRNA is expressed by In the network composed of lncRNA and miRNA, B represents the original interaction information between lncRNA and miRNA node pairs, i.e., the one-hop information, and Finally, we took the second row of matrix g matrix .S. In addition, to fully learn the edge information, we applied wide convolution by padding zeros before convolution. The definitions of ith row and jth column element of the embedded layer S, and kth filter is slid to position Because the left and right convolution processes are similar, we will only describe the left convolution process in detail herein. In the pooling layer, After two convolutions and pooling were completed, we obtained the final representation H is the weight matrix between the fully connected layer and the output layer, and Finally, we flattened K is the weight matrix between the fully connected layer and the output layer, and Similarly, we employed To fully utilize the dual prediction score matrix, we designed a dual combination strategy to train the model and obtain the final prediction score. We used M represents the number of training samples, and a and b represent the probabilities obtained by the Softmax function. The dual convolution and combining processes are displayed in The loss functions of the left and right CNNs can be defined asLDAPred, which is a new method based on a dual convolutional neural network, was developed to predict the potential associations between lncRNAs and diseases. According to the biological premise that lncRNAs are likely to possess associations with diseases, the embedding layer was established from a biological perspective. The left and right embedding layers capture the original similarities, associations, and interactions among lncRNAs, miRNAs, and diseases, as well as the topological structures of bi-layer networks. The original representation of lncRNA–disease pairs and their network representations were learned by the new framework based on dual convolutional neural networks and information flow propagation. Cross-validation results for 405 diseases and case studies on three diseases indicated that LDAPred has a strong ability to predict potential associations between lncRNAs and diseases.


Long non-coding RNAs play an important role in human complex diseases. Identification of lncRNA-disease associations will gain insight into disease-related lncRNAs and benefit disease diagnoses and treatment. However, using experiments to explore the lncRNA-disease associations is expensive and time consuming.Integrating Diverse Heterogeneous Information sources with positive pointwise Mutual Information and Random Walk with restart algorithm (namely IDHI-MIRW). IDHI-MIRW first constructs multiple lncRNA similarity networks and disease similarity networks from diverse lncRNA-related and disease-related datasets, then implements the random walk with restart algorithm on these similarity networks for extracting the topological similarities which are fused with positive pointwise mutual information to build a large-scale lncRNA-disease heterogeneous network. Finally, IDHI-MIRW implemented random walk with restart algorithm on the lncRNA-disease heterogeneous network to infer potential lncRNA-disease associations.In this study, we developed a novel method to identify potential lncRNA-disease associations by https://github.com/NWPU-903PR/IDHI-MIRW.Compared with other state-of-the-art methods, IDHI-MIRW achieves the best prediction performance. In case studies of breast cancer, stomach cancer, and colorectal cancer, 36/45 (80%) novel lncRNA-disease associations predicted by IDHI-MIRW are supported by recent literatures. Furthermore, we found lncRNA LINC01816 is associated with the survival of colorectal cancer patients. IDHI-MIRW is freely available at The online version of this article (10.1186/s12859-019-2675-y) contains supplementary material, which is available to authorized users. Long non-coding RNAs (lncRNAs) are the biggest part of non-coding RNAs with at least 200 nucleotides and no observed potential to encode proteins , 2. To dIn recent years, the number of experimentally verified lncRNA-disease associations is gradually increasing. Several databases for lncRNA functions and disease associations have been published, such as LncRNAdb , LncRNADThe network-based methods, such as RWRlncD , RWRHLD To address the aforementioned issues (or limitations) and further improve the prediction accuracy, we proposed a novel network-based method, namely IDHI-MIRW, to predict the potential lncRNA-disease associations by constructing a large-scale lncRNA-disease heterogeneous network with Random Walk with Restart (RWR) algorithm and the positive pointwise mutual information (PPMI). Instead of constraining lncRNA and disease on those with at least one known lncRNA-disease association, IDHI-MIRW calculates the lncRNA similarities for all the lncRNAs involved in lncRNA expression profiles, lncRNA-miRNA interactions, and lncRNA-protein interactions, and also calculates the diseases similarities for all the diseases involved in disease ontology, disease-miRNA associations, and disease-gene associations. Then, IDHI-MIRW uses the RWR algorithm on each similarity network to capture network topological structural features for measuring the lncRNA/disease topological similarity through the PPMI. By integrating the lncRNA/disease topological similarity, and introducing the known lncRNA-disease association information, a large-scale lncRNA-disease heterogeneous network is built. Finally, the random walk with restart on heterogeneous network (RWRH) algorithm  is appliIn this section, we first introduced the evaluation method and metrices for evaluating the performance of the IDHI-MIRW method. Then, we compared our IDHI-MIRW method with other existing state-of-the art methods on a small-scale lncRNA-disease heterogeneous network, explored the predictive power of IDHI-MIRW on a large-scale lncRNA-disease heterogeneous network, and discussed the effect of different parameters. In the end, we analyzed several predicted potential lncRNA-disease associations with our IDHI-MIRW.di, each known lncRNA associated with di is left out in turn as a test sample, and corresponding association edge between test lncRNA and di is removed, and the remaining lncRNAs associated with di are considered as training samples.The leave-one-out cross validation (LOOCV) test method was used to evaluate the performance of the IDHI-MIRW method. In LOOCV test method, each known lncRNA-disease association in the dataset is singled out in turn as a test sample, and the remaining lncRNA-disease associations are used as training samples. That is, for a given disease The area under the receiver operating characteristic (ROC) curve (AUC) and the area under the precision-recall (PR) curve (AUPR) were used as evaluation metrices in our experiments. The ROC curve is the plot of the true-positive rate  versus the false-positive rate (FPR) at different rank cutoffs. The PR curve is the plot of the ratio of true positives among all positive predictions for each given recall rate.S) which contains 362 lncRNAs, 370 diseases, and 2169 known lncRNA-disease associations. Most existing methods often built this small-scale lncRNA-disease heterogeneous network in which each lncRNA (or disease) has at least an associated disease (or lncRNA) to predict the potential lncRNA-disease associations. LRLSLDA [We compared our IDHI-MIRW method with other six state-of-the-art methods of LRLSLDA , LNCSIM  LRLSLDA  and LNCS LRLSLDA  adopt th LRLSLDA , IRWRLDA LRLSLDA , KATZLDA LRLSLDA  and GrwL LRLSLDA  are the To further evaluate the performance of IDHI-MIRW for predicting the associated lncRNAs for new diseases without any known lncRNA association information, we removed all the known lncRNA associations for the query disease in the small-scale lncRNA-disease heterogeneous network. Due to RWRlncD implemented the RWR algorithm on an lncRNA similarity network, we just compared our IDHI-MIRW method with other five methods of LRLSLDA, LNCSIM, IRWRLDA, KATZLDA and GrwLDA for predicting the associated lncRNAs of the query diseases. The comparison results are shown in Fig. L) by introducing 2169 known lncRNA-disease associations, then implemented our IDHI-MIRW method on HNetL. Additional files S and HNetL heterogeneous networks in LOOCV test are listed in Table In order to illustrate the effectiveness of introducing multiple information sources, we collected 7637 lncRNAs and 6453 diseases from EMBL-EBI E-MTAB-5214), starBase v2.0 , NPInter4, starBaS) and large-scale ncRNA-disease heterogeneous network (HNetL) in LOOCV test are shown in Table In order to evaluate the effectiveness of using the topological similarity network to construct the lncRNA-disease heterogeneous network for improving the predictive performance, we designed another method of IDHI-AVG by adopting the strategy of averaging three lncRNA similarity matrices of LncNet1, LncNet2 and LncNet3 to form the lncRNA integration network , averaging of three disease similarity matrices of DisNet1, DisNet2, and DisNet3 to form the disease integration network . IDHI-AVG combines these two integration similarity networks of LncINet and DisINet with known lncRNA-disease bipartite network to construct the lncRNA-disease heterogeneous network on which RWRH algorithm is implemented to predict the potential lncRNA-disease associations. The compared results of IDHI-AVG and IDHI-MIRW on the small-scale lncRNA-disease heterogeneous network . Additional file S heterogeneous network in LOOCV test. In this work, we selected α = 0.9, γ = 0.9, η = 0.2, and β = 0.6.There are four main parameters in our method, which are the restart probability We used breast cancer, stomach cancer, and colorectal cancer as the cases to predict their potential associated lncRNAs with our IDHI-MIRW. For a given disease, all known lncRNAs associated with this given disease were considered as the seed nodes, and other remaining lncRNAs  were considered as the candidates associated with the given disease. By implementing our IDHI-MIRW algorithm on the large-scale lncRNA-disease heterogeneous network, and according to the lncRNA-disease associations ranking scores from large to small, we extract top 15 potential association lncRNAs for each cancer. These top potential association lncRNAs are listed in Additional files For breast cancer which is one of most common cancers and the second leading cause of cancer death , 13 out For stomach cancer (or gastric cancer) which is the third leading cause of cancer mortality in the world , 54, 11 For colorectal cancer which is the third most commonly diagnosed cancer in males and the second in females , 12 out To further discover the evidences for the predicted lncRNAs associated with cancers, we analyzed the RNAseq and clinical data from TCGA for breast cancer, stomach cancer and colorectal cancer. For colorectal cancer, the RNASeq data including 19,676 protein coding genes, 15,513 lncRNA genes in 41 normal samples and 474 tumor samples were downloaded from TCGA. Using DESeq2  algorithAdditional files In summary, 36  out of 45 potential association lncRNAs have been supported by recent literatures. By analyzing the nine unvalidated potential association lncRNAs, we found that six lncRNAs are differentially expressed in corresponding cancers, and lncRNA LINC01816 is associated with the survival of patients with colorectal cancer. Results of these three case studies show that IDHI-MIRW can effectively predict the new association lncRNAs for a disease.LncRNAs play important roles in the development of human complex diseases. More and more attentions have been paid to discover the lncRNA functions related with human complex disease. Most previous computational methods only focus on the small-scale lncRNA-disease heterogeneous network  to predict the lncRNA-disease associations. To address this issue, IDHI-MIRW was developed to predict the potential lncRNA-disease associations based on a large-scale lncRNA-disease heterogeneous network (containing 7637 lncRNAs and 6453 diseases). Instead of calculating similarities of lncRNAs and diseases only involving in known lncRNA-disease associations, IDHI-MIRW used three lncRNA-related information  to form three lncRNA similarity networks, and three disease-related information  to form three disease similarity networks. Furthermore, instead of directly fusing those similarity networks, IDHI-MIRW applied the RWR algorithm on each lncRNA/disease similarity network to capture the topological similarity, and the PPMI to generate lncRNA/disease topological similarity network. The large-scale lncRNA-disease heterogeneous network was constructed by combing the lncRNA topological similarity network, disease topological similarity network, and the known lncRNA-disease bipartite graph. Then, the RWRH algorithm was used to prioritize candidate lncRNAs for each query disease. Our experiment results show that IDHI-MIRW achieves a better performance than other existing methods. We evaluated the effectiveness of introducing multiple information sources and capturing topological similarities, Tables Although IDHI-MIRW can effectively predict potential lncRNA-disease associations, there are still several issues need to be further addressed in the future. First, IDHI-MIRW used three lncRNA-related and three disease-related information to generate similarity matrices, we still expect to integrate more information  to better predict lncRNA-disease association. Second, the averaging strategy was used to integrate the lncRNA/disease topological similarity matrices, we expect to design better integration approaches in future work to measure the different contributions of multiple lncRNA/disease similarities.In this study, we proposed a novel network-based method (namely IDHI-MIRW) for identifying potential lncRNA-disease associations. We built a large-scale lncRNA-disease heterogeneous network by integrating multiple lncRNA-related information , multiple disease-related information , and known lncRNA-disease association information using RWR and PPMI. Our experimental results show that IDHI-MIRW can achieve higher performance than other state-of-the-art methods, and we found lncRNA LINC01816 is associated with the survival of colorectal cancer patients. These results indicate that IDHI-MIRW will contribute to the identification of potential lncRNA-disease associations.We collected lncRNA expression profile, lncRNA-miRNA interaction, and lncRNA-protein interaction data for constructing the lncRNA similarity networks, and Diseases Ontology (DO) information, disease-miRNA association, and disease-protein association data for constructing the disease similarity networks. All lncRNAs are annotated by ensembl gene ID, and all diseases are annotated by Disease Ontology ID.LncRNA expression profiles were downloaded from EMBL-EBI (E-MTAB-5214), which includes the expression profiles in 53 human tissue samples. LncRNA-miRNA interactions and lncRNA-protein interactions were collected from starBase v2.0 , NPInterOur IDHI-MIRW algorithm consists of the following four steps. Step 1, build three lncRNA similarity networks  based on lncRNA expression profiles, lncRNA-miRNA interactions, and lncRNA-protein interactions, and also build three disease similarity networks  based on disease ontology, disease-miRNA associations, and disease-gene associations. Step 2, form the lncRNA topological similarity network (LncTSNet) and disease topological similarity network (DisTSNet) by fusing lncRNA and disease multiple topological similarities obtained through implementing RWR on lncRNA similarity network  and disease similarity network , respectively. Step 3, construct a large-scale lncRNA-disease heterogeneous network by integrating lncRNA topological similarity network (LncTSNet), disease topological similarity network (DisTSNet), and known lncRNA-disease associations. Step 4, implement RWRH on the lncRNA-disease heterogeneous network for predicting the potential lncRNA-disease associations. The flowchart of IDHI-MIRW is shown in Fig. P-value threshold (< 0.01), we built the LncNet1 lncRNA similarity weighted network. Based on Gaussian interaction profile kernel similarity [li and lncRNA lj, then built the LncNet2 and LncNet3 lncRNA similarity weighted networks, respectively. Gaussian interaction profile kernel similarity between lncRNA li and lncRNA lj is calculated.IP(li) is the binary vector of lncRNA-miRNA (or lncRNA-protein) interactions encoding the presence or absence of interactions between lncRNA li and miRNA (or protein) in the lncRNA-miRNA (or lncRNA-protein) interaction dataset, κl controls the kernel bandwidth, and Nl is the total number of lncRNAs.By calculating the Pearson correlation coefficient of any lncRNA pair with expression profiles and fixing the milarity , 63 of ldi and dj, then built the DisNet2 and DisNet3 disease similarity weighted networks, respectively.IP(di) is the binary vector of disease-miRNA (or disease-gene) associations encoding the presence or absence of associations between di and miRNA (or gene) in the disease-miRNA (or disease-gene) association dataset. κd controls the kernel bandwidth, and Nd is the total number of diseases.Based on the structure of a directed acyclic graph (DAG) in Disease Ontology, we used the function “doSim” form R package “DOSE”  to obtaiSt is the distribution matrix in which the -th element denotes the distribution probability of node j being visited from node i after t iterations in the random walk process and S0 is the initial distribution matrix in which S0 = 1, S0 = 0, ∀j ≠ i. α is restart probability controlling the relative influence of local and global topological information. B is the weighted adjacency matrix of lncRNA (or disease).Instead of directly fusing six similarity networks , we captured the network topological structural features by implementing the RWR algorithm on each similarity network. The RWR algorithm is a network diffusion algorithm, which has been extensively applied to analyze the complex biological network –69. By cS = St + 1 − Stis less than a small positive ε (we set ε = 10−10), we can obtain a stationary distribution matrix S, which was referred as the diffusion state of each node [S in diffusion state matrix S represents the probability of RWR starting node i and ending up at node j in equilibrium. When the diffusion states of two nodes are close, which suggests that they may have similar positions with respect to other nodes in the network and they probably share similar functions.When the L1 norm of Δach node . The eleMotivated by Gligorijevic et.al. , we thenMI is a non-symmetric matrix, thus we use the average of MI and MI to represent the topological similarity of node i and node j. After obtaining three lncRNA topological similarity matrices The matrix AL and AD represent the weighted adjacency matrices of LncTSNet and DisTSNet, respectively; ALD is the adjacency matrix of the lncRNA-disease bipartite graph; ADL represents the transpose of ALD. If there is association between lncRNA i and disease j in known lncRNA-disease associations, ALD = 1, otherwise, ALD = 0.By integrating the LncTSNet and DisTSNet networks with known lncRNA-disease bipartite network, we can construct the lncRNA-disease heterogeneous network whose adjacency matrix can be defined as:pt is a probability vector in which the i-th element holds the probability of finding the random walker at node i at step t; β ∈  is restart probability; p0 is the initial probability vector for lncRNA-disease heterogeneous network which is defined as u0 and v0 represent the initial probability of LncTSNet and DisTSNet, respectively. The initial probability u0 of LncTSNet network is set such that all the seed nodes are assigned to the equal probabilities with the sum of probabilities equal to 1. Similarity, the initial probability v0 of DisTSNet network is given. The parameter η ∈  is used to weight the importance of each subnetwork.To predict the association between lncRNA and disease, we adopted the RWRH  algorithm  to priorML and MD are the intra-subnetwork transition matrices, MLD and MDL are the inter-subnetwork transition matrices. Let γ be the jumping probability, that is, the probability of random walker jumping from lncRNA network to disease network or vice versa. Thus, the transition probability ML from lncRNA li to lncRNA lj and the transition probability MD  from disease di to disease dj are defined asli to disease dj and the transition probability from disease di to lncRNA lj are described as:The transition probability from lncRNA p∗ = p∞ can be obtained by performing the iteration until the difference between pt and pt + 1 (measured by the L1 norm) fall below 10−10. p∗ gives the ranking score of every lncRNA for a query disease. The lncRNAs with maximum in p∗ are considered as the most probable associated lncRNAs of the query disease.After some steps, the steady state probability vector Additional file 1:LncRNA data processing procedure. (TIF 1447 kb)Additional file 2:Disease data processing procedure. (TIF 1340 kb)Additional file 3:α. (B) AUC values with different γ. (C) AUC values with different η. (D) AUC values with different β. (E) AUPR values with different α. (F) AUPR values with different γ. (G) AUPR values with different η. (H) AUPR values with different β. (TIF 3520 kb)AUPR values of IDHI-MIRW on the large-scale lncRNA-disease heterogeneous with different parameters in LOOCV test. (A) AUC values with different Additional file 4:α. (B) AUC values with different γ. (C) AUC values with different η. (D) AUC values with different β. (E) AUPR values with different α. (F) AUPR values with different γ. (G) AUPR values with different η. (H) AUPR values with different β. (TIF 3705 kb)AUC and AUPR values of IDHI-MIRW on the small-scale lncRNA-disease heterogeneous with different parameters in LOOCV test. (A) AUC values with different Additional file 5:The top 15 predicted associated lncRNAs for breast cancer. (XLSX 9 kb)Additional file 6:The top 15 predicted associated lncRNAs for stomach cancer. (XLSX 9 kb)Additional file 7:The top 15 predicted associated lncRNAs for colorectal cancer. (XLSX 9 kb)Additional file 8:The results of RNASeq data analysis for breast cancer. (A) heatmap of top 200 most significantly dysregulated lncRNA expression values. (B) heatmap of lncRNA AL157395.1 expression values. (C) boxplot of lncRNA AL157395.1 expression in normal and tumor samples. (D) heatmap of lncRNA AP001528.1 expression values. (E) boxplot of lncRNA AP001528.1 expression in normal and tumor samples. (TIF 9850 kb)Additional file 9The results of RNASeq data analysis for stomach cancer. (A) heatmap of top 200 most significantly dysregulated lncRNA expression values. (B) heatmap of lncRNA KCNQ1OT1 expression values. (C) boxplot of lncRNA KCNQ1OT1 expression in normal and tumor samples. (D) heatmap of lncRNA DLEU2 expression values. (E) boxplot of lncRNA DLEU2 expression in normal and tumor samples. (F) heatmap of lncRNA LINC00299 expression values. (G) boxplot of lncRNA LINC00299 expression in normal and tumor samples. (TIF 9211 kb)Additional file 10:The predicted lncRNA-disease associations. (TXT 180 kb)Additional file 11:Details and statistics of collected data. (DOCX 34 kb)


Current studies have shown that long non-coding RNAs (lncRNAs) play a crucial role in a variety of fundamental biological processes related to complex human diseases. The prediction of latent disease-lncRNA associations can help to understand the pathogenesis of complex human diseases at the level of lncRNA, which also contributes to the detection of disease biomarkers, and the diagnosis, treatment, prognosis and prevention of disease. Nevertheless, it is still a challenging and urgent task to accurately identify latent disease-lncRNA association. Discovering latent links on the basis of biological experiments is time-consuming and wasteful, necessitating the development of computational prediction models. In this study, a computational prediction model has been remodeled as a matrix completion framework of the recommendation system by completing the unknown items in the rating matrix. A novel method named faster randomized matrix completion for latent disease-lncRNA association prediction (FRMCLDA) has been proposed by virtue of improved randomized partial SVD (rSVD-BKI) on a heterogeneous bilayer network. First, the correlated data source and experimentally validated information of diseases and lncRNAs are integrated to construct a heterogeneous bilayer network. Next, the integrated heterogeneous bilayer network can be formalized as a comprehensive adjacency matrix which includes lncRNA similarity matrix, disease similarity matrix, and disease-lncRNA association matrix where the uncertain disease-lncRNA associations are referred to as blank items. Then, a matrix approximate to the original adjacency matrix has been designed with predicted scores to retrieve the blank items. The construction of the approximate matrix could be equivalently resolved by the nuclear norm minimization. Finally, a faster singular value thresholding algorithm with a randomized partial SVD combing a new sub-space reuse technique has been utilized to complete the adjacency matrix. The results of leave-one-out cross-validation (LOOCV) experiments and 5-fold cross-validation (5-fold CV) experiments on three different benchmark databases have confirmed the availability and adaptability of FRMCLDA in inferring latent relationships of disease-lncRNA pairs, and in inferring lncRNAs correlated with novel diseases without any prior interaction information. Additionally, case studies have shown that FRMCLDA is able to effectively predict latent lncRNAs correlated with three widespread malignancies: prostate cancer, colon cancer, and gastric cancer. Long non-coding RNAs are RNA molecules whose transcripts are not less than 200 nucleotides, including intronic/exonic lncRNAs, antisense lncRNAs, overlapping lncRNA and long intergenic ncRNAs (lincRNAs). LncRNAs have long been considered as transcriptional noise, because of their absence in encoding proteins. Recently, it has been found that some lncRNAs regulate the expression of target genes after transcription, whose malfunction may lead to a number of diseases. For example, abnormal lncRNA expression may be involved in certain stages of cancer progression, which can serve as a potential biomarker for early tumor diagnosis . In addiIn the first major category, models for single-interaction data sources are based on diseases-lncRNAs interaction (association/link) data, which is unique known interaction information. According to its method, the model can be divided into two minor branches. The first minor branch is composed of machine-learning based models, in which the prediction of latent disease-lncRNA association takes experimentally validated disease-lncRNA associations as labeled data (training set) and unknown associations as unlabeled samples . For example, a method named Laplacian Regularized Least Squares (LRLSLDA) was first proposed by Chen et al. to infer disease-lncRNA associations with a semi-supervised learning model . It is aAdditionally, the second minor branch is composed of network-based models, random walk and a variety of propagation algorithms implemented on a heterogeneous network to infer latent disease-lncRNA associations. The heterogeneous network is constructed by integrating lncRNA-disease interaction network, disease similarity network and lncRNA similarity network. For instance, based on the hypothesis that functional lncRNAs are associated with diseases with similar phenotypes, a lncRNA functional similarity network (LFSN) is constructed and a novel computational framework RWRlncD is proposed for predicting latent disease-lncRNA associations through random walk with restart . HoweverDue to the fact that known experimentally validated disease-lncRNA interactions are still rare, the second major category of computational models is models based on multi-interaction data sources proposed for association prediction. Multi-interaction data sources, such as lncRNA-gene interaction, lncRNAs-miRNAs interaction, disease-gene interaction and miRNA-disease interaction, are also included to infer latent disease-lncRNA associations. For example, a TPGLDA method is proposed to identify the underlying relationships by a tripartite graph of disease-lncRNA-gene and to develop an efficient resource allocation algorithm in the graph . TPGLDA Inferring latent disease-lncRNA association can also be modeled as a recommendation system which recommends top-ranked lncRNAs for given diseases. Based on matrix completion, the establishment of the disease-lncRNA recommendation system aims to complete unknown terms in the association matrix according to its ranked scores. Similar to the hypothesis in the user-item recommendation system that users with similar behaviors prefer similar items, the prediction of disease-lncRNA association assumes that phenotypically similar diseases tend to interact with functionally similar lncRNAs . In our Our work main contributions are threefold: first, the integrated similarities for diseases and lncRNAs were properly calculated by different methods dealing with different types of data sources. The ratio of cosine similarity in integrated similarity was better determined by learning, which extracted similarity information based on known disease-lncRNA interaction. Therefore, FRMCLDA was able to offset biased predictions by similarity integration which was not entirely dependent on known interaction. Second, diseases and lncRNAs were mapped into the same network by constructing a heterogeneous bilayer network. FRMCLDA completed the disease-lncRNA interaction matrix by completing the adjacency matrix of the large-scale heterogeneous network. Thus, the similar information was included in the association prediction. Third, we took advantage of an effective fSVT algorithm which adopted rSVD-BKI and a novel subspace reuse technique to expeditiously approximate the dominant singular values and homogeneous singular vectors in an adaptive manner. Hence, the recommendation system could be extended for comprehensive adjacency matrices of heterogeneous bilayer networks.For evaluating the performance of our method, cross validation experiments were performed on three benchmark databases, Dataset 1, Dataset 2 and Dataset 3. FRMCLDA obtained reliable AUCs of 0.92068, 0.91224 in global LOOCV and local LOOCV respectively in Dataset1, at least 5% higher than other comparison models. In Dataset 2 and Dataset 3, FRMCLDA achieved an AUC of 0.9182 and 0.8999 by global 5-fold CV, higher than other comparison methods. In addition, a case study on inferring latent lncRNAs associated with prostate cancer, colon cancer and gastric cancer in Dataset 3 were performed. In terms of the results, 16, 15, 16 out of the top 20 predicted lncRNAs associated with prostate cancer, colon cancer and gastric cancer respectively were confirmed by recent literature and public databases. The results show that FRMCLDA is able to effectively infer the associations between diseases and lncRNAs with higher accuracy than the other existing models.id is associated with lncRNA jl, the value of DL in the association matrix is 1. And if the link between id and jl is unknown or uncertain, DL is 0. It is noted that the unlinked evidence between id and jl is difficult to obtain. The known experimentally validated disease-lncRNA links can be retrieved from the public association database, based on which disease-lncRNA interaction matrix DL is established. If the number of nonzero elements is far smaller than that of zero elements, and the distribution of nonzero elements in the matrix is irregular, the matrix will be called sparse matrix. Generally, matrix DL is a sparse matrix, because, due to the insufficient number of studies, there are much more unknown associations  in matrix DL than known ones   see Table 1.In our work, a new method called FRMCLDA is proposed to infer latent disease-lncRNA associations on the basis of fast matrix completion. The mechanism of FRMCLDA is shown in All the known diseases-lncRNA interactions were obtained from three gold standard databases in three benchmark datasets respectively: MNDR database, Lnc2Cancer database and LncRNADisease database .The known associations between lncRNA and disease in Dataset 1 were retrieved from the MNDR database in 2015. After removing all the duplicate records of lncRNAs and diseases, and what do not belong to human beings, and correcting the names of the lncRNAs  and diseases , we finalized 352 disease-lncRNA associations, including 156 lncRNAs and 190 diseases.In Dataset 2, the known associations between lncRNA and disease were obtained from the Lnc2Cancer database in 2016. After eliminating the duplicate associations on account of different evidences, we obtained 540 distinct known disease-lncRNA associations, including 115 lncRNAs and 178 diseases.http://cmbi.bjmu.edu.cn/lncrnadisease) in 2015. In the same way as data preprocessing, we downloaded 621 known disease-lncRNA associations, including 248 lncRNAs and 226 diseases. The details of the three datasets are shown in In Dataset 3, the known disease-lncRNA associations were downloaded from the manually curated LncRNADisease database  is utilized to label a disease, which includes overall relevant annotation labels acquired from the U.S. National Library of Medicine (MeSH)  e (MeSH) . It is aontology .Disease functional similarity: Disease functional similarity was calculated using the Jaccard similarity coefficient on account of gene-gene ontology relationships and disease-gene relationship, as reported in previous studies , and can be calculated by formula (1):b)  studies . DiseaseGOdi represents the gene ontology terms related to disease di, and the symbol |·| represents the number of items in a set. where Disease cosine similarity: Widely used in information retrieval and data mining, the cosine similarity is a popular method for calculating the similarity as the cosine of the angle between vectors. Here we used cosine similarity to extract disease feature information from the known interaction matrix DL. The disease cosine similarity denoted as DS–cosinecan be calculated by formula (2):c) IP(id) is the interaction profile of disease id, the i-th row vector of the interaction matrix DL. If disease id is associated with lncRNA kl, the k-th element in IP(id) is 1, otherwise the value is 0. The value 0 does not mean that association does not exist but means it is uncertain. ||IP(id)|| is the 2-norm of IP(id). where Integrated disease similarity DS: To illustrate the adaptability of our model to different similarity data, we adopted two different integrated disease similarities in three benchmark datasets. In Dataset 1 and Dataset 2, DS was calculated by: DS was calculated by: d) To calculate lncRNA integrated similarity, we adopted four different sources of similarity data: lncRNA sequence similarity, lncRNA expression similarity, lncRNA functional similarity and lncRNA cosine similarity.LncRNA sequence similarity: Most of the RNA sequences of lncRNAs were downloaded mainly from the database LncRNADisease (http://www.cuilab.cn/lncrnadisease). The sequences not available in LncRNADisease were retrieved from the databases UCSC and LNCipedia. The sequence similarity between two lncRNAs were calculated with Needleman-Wunsch global alignment algorithm (Emboss-Needle tool) (a) le tool) . We set SW is the alignment score calculated by Emboss-Needle, which is equal to the sum of the matches taken from the scoring matrix, minus penalties arising from opening and extending gaps in the aligned sequences. where LncRNA expression similarity: The expression profiles of lncRNA can be obtained from the dataset E-MTAB-513 in ArrayExpress (LS_exp with the absolute Spearman correlation coefficient (b) yExpress . Based officient .LncRNA functional similarity: Based on an accepted assumption that lncRNAs with similar functions have similar interaction patterns to those of diseases, the functional similarity of lncRNA can be obtained via computation of disease semantic similarity from a previous study by il is correlated with a set of diseases iD = {id1, id2,…, dim}, and lncRNA jl is correlated with a set of diseases Dj = {jd1, jd2,…jnd}. Semantic similarity between ild and Dj is calculated as formula (4):c)  And then the functional similarity of lncRNAs can be computed as formula (5):LS_func denotes the functional similarity between lncRNA il and lncRNA jl. where LncRNA cosine similarity: In the same way, we used cosine similarity to extract lncRNA feature information from the known interaction matrix DL. lncRNA cosine similarity can be calculated as formula (6):d) IP(jl) is resulted from the j-th column of the interaction matrix DL. IP(jl) is a vector which denotes the feature vector for lncRNA jl. where Integrated lncRNA similarity LS: In three benchmark datasets, we adopted three different similarity computation methods to fully demonstrate the robustness of FRMCLDA. In Dataset 1, integrated lncRNA similarity LS was calculated as: LS was calculated as: LS was calculated as: e) DS and LS calculated above, disease similarity network and lncRNA similarity network can be constructed. Let D = {d1, d2,…, nd} represent the set of n diseases in the disease similarity network. The edge between disease id and jd is weighted by integrated disease similarity DS. Let L = {l1, l2,…, ml} represent the set of m lncRNAs in the lncRNA similarity network. The edge between lncRNA il and jl is weighted by integrated lncRNA similarity LS. Besides, the disease-lncRNA interaction network can be modeled as G, where V(G) = {D,L}, E(G) ⊆ D × L, E(G) = {ije, edge between disease id and lncRNA jl}. The edge ije is initialized to 1, if there exists a known link between disease id and lncRNA jl, otherwise, ije is initialized to 0. DL is the adjacency matrix for the disease-lncRNA interaction network.Based on the integrated disease and lncRNA similarity matrices via disease-lncRNA association network, as shown in Finally, a heterogeneous bilayer network is constructed by connecting disease similarity network and lncRNA similarity network DS and LS are the adjacency matrix of the disease similarity network and the lncRNA similarity network. The off-diagonal sub-matrix DL is the adjacency matrix for the disease-lncRNA interaction network, TDL is the transpose of DL. Usually, the interaction between lncRNAs and diseases is mutual, and values of the matrix DL are nonnegative, therefore the adjacent matrix A is meristic and positive semi-definite. The singular values of the adjacent matrix A are nonnegative real numbers and equivalent to the eigenvalues. In conclusion, the prediction of disease-lncRNA association can be remodeled as the matrix completion of the adjacency matrix A. If matrix A is only comprised of matrix DL, rather than the large-scale matrix of the heterogeneous network, then the completion based on rank minimization will not generate significant results. That is because all known disease-lncRNA associations are positive in matrix. Only restoring the matrix DL will result in an optimized solution to rank minimization problem, i.e., all-one matrix with rank 1.where diagonal sub-matrices A* with the same size (m + n) × (m + n). It is assumed that A have rank r(r ≪ (m + n)). Φ is denoted as an index set for all the known entries of matrix A. The problem of matrix completion can be converted to solving the rank minimization problem by formula (8):Our goal is to restore the unknown entries of the adjacent matrix A by constructing a proximate matrix PΦ(A) is denoted as an orthogonal projector onto the span of matrix A. Its value is 0 when the element  is not in the set Φ. Matrix A is the adjacency matrix of the heterogeneous network constructed in Datasets and Data Processing. However, the problem of rank minimization is generally considered as a NP-hard problem (*) of the matrix, which is known to be solved by standard singular value threshold (SVT) algorithm (in which  problem . An apprlgorithm . Therefolgorithm . MinimizEquation (9) can be solved by the iterative processes in formula (10) and (11):shrink(Y(i), τ) is a soft thresholding operator that computes the singular value of the matrix Y at level τ (Yat the ith iteration. Y(i) is usually a relatively large matrix with high sparsity, and usually can be stored with a sparse matrix. Starting computation from X(i) and Y(i) can be generated through the linearized Bregman iteration.where the function  level τ . δ is thflops) with the runtime benefit. Faster matrix completion even incorporates a block Krylov sub-space iteration rSVD-BKIr scheme and a novel sub-space reuse mechanism  (LD. Our faster randomized matrix completion method is illustrated in Algorithm 1 in the rSVD-BKI(·) performs singular value decomposition and the details of realization can be found in an earlier study  . The faseration) . FRMCLDAer study We first put forward the evaluation metrics for the methods of association prediction. Second, we tested the effects of cosine similarity on diseases and lncRNAs and fine-tuned the weights of cosine similarity. Third, we implemented permutation test to assess the influence of different data sources on optimization procedure. Fourth, we recorded the time usage of FRMCLDA for different sizes of heterogeneous network. Fifth, we compared FRMCLDA with other existing methods by global LOOCV experiments, local LOOCV experiments and global 5-fold cross-validation experiments. Finally, we implemented case studies to validate the practicability of FRMCLDA.In order to assess the performance of FRMCLDA in inferring latent disease-correlated lncRNAs, global LOOCV experiments, local LOOCV experiments and global 5-fold cross-validation experiments are implemented on three benchmark datasets. Under the framework of LOOCV, each known experimentally validated association is picked in turns as a test sample, and all the other known associations are considered as training samples. The test sample is sorted together with the candidate samples without known association evidence. The test sample whose rank exceed the given threshold would be considered as a successful prediction. The main difference between global and local LOOCV is whether to investigate all diseases simultaneously or only query one disease at a time to select candidate samples. That is to say, global LOOCV considered all the unknown associations as candidate samples, whereas local LOOCV only focused on one disease in the test sample and selected the corresponding unknown associations as candidate samples. In global 5-fold cross-validation, all of the known experimentally validated associations are divided into five uncrossed sets, whose size must be strictly equal. Each set of the five is taken in turns as the test sample, but the other 4 sets are served as training samples. After matrix completion is performed, the test samples are ranked together with candidate samples and then are sorted in the descending order of their predicted scores.Furthermore, false negative (FN), false positive (FP), true negative (TN) and true positive (TP) are summarized based on the ranked results for each specific threshold. The receiver operating characteristic curves are made by plotting the true positive rate  against false positive rate (FPR) based on varying thresholds. The precision-recall (PR) curve is also plotted to fully evaluate the performance of the prediction. The area under the ROC curve (AUC) and the area under the PR curve (AUPR) are finally calculated to evaluate the overall performance of the prediction. An AUC value of 0.5 implies a random prediction and an AUC value of 1 implies a perfect prediction performance. Therefore, AUC and AUPR are used as primary evaluating measures.lw and dw in integrated similarity calculations can be fine-tuned by cross validation in three benchmark datasets separately. Let dw and lw vary from 0.1 to 1 at the increment of 0.1. According to AUC values of LOOCV based on Dataset1, FRMCLDA performed best when dw1 = 0.9 and lw1 = 0.7. Likewise, on Dataset2, we chose dw2 = 0.7 and lw2 = 0.5. On Dataset3, we chose dw3 = 0.5 and lw3 = 0.5. All can be seen in Both integrated disease similarity and integrated lncRNA similarity in three benchmark datasets are calculated with cosine similarity combined, which can extract feature information from the known interaction matrix. The weights of In Dataset2, we implemented 5-fold cross validation 20 times to test the effects of the cosine similarity on model performance. The four test settings were: 1) using cosine similarity both for integrated similarity of neither lncRNAs nor diseases; 2) only the lncRNA similarity integrating the cosine similarity; 3) only the disease similarity integrating the cosine similarity; 4) both lncRNA similarity and disease similarity integrating cosine similarity. The results can be seen in To evaluate the influence of different data sources on the optimization procedure of matrix completion, we have implemented a permutation test on disease-lncRNA interaction matrix DL, lncRNA similarity matrix LS, and disease similarity matrix DS separately. Based on the LOOCV framework, we randomized each of the three matrices in turns, while keeping the other two matrices unchanged. We carried it out 20 times and recorded the average AUC value for each type of data source. Usually, if a matrix contributes more to the optimization procedure, the result of the permutation test based on it will be closer to the stochastic value. As shown in In FRMCLDA model, we implement matrix completion through fSVT as proposed by a previous study . Algoritp = 2 and ε = 0.4. We executed 20 times FRMCLDA in three benchmark datasets. Average time usage of FRMCLDA and standard deviations are shown in Here, we set On Dataset 1, the performance of FRMCLDA is compared with four popular methods: LRLSLDA , KATZLDAOne advantage of FRMCLDA is that it is able to infer latent correlated lncRNAs with queried diseases, even novel diseases. To show the performance of FRMCLDA in predicting novel disease-correlated lncRNAs, we implemented local LOOCV on Dataset 1. The results of FRMCLDA with local LOOCV on Dataset 1 were recorded in The robustness of FRMCLDA was further validated by inferring latent associations on Dataset 2 and Dataset 3. We conducted 20 times global 5-fold cross-validation experiments to validate the precision of prediction by FRMCLDA on Dataset 2 and Dataset 3. The results of ROC curve, PR curve, precision-rank bars and recall-rank bars using different methods are shown in After performing cross validation to confirm the ability of FRMCLDA, we conducted a global prediction of potential related disease-lncRNA pairs. All known lncRNAs-diseases links were considered as training samples, while other unknown associations constituted the candidate samples. FRMCLDA can infer the latent correlated lncRNAs for all diseases simultaneously by faster random matrix completion. All candidate lncRNAs correlated with a queried disease were ranked according to predicted scores generated by FRMCLDA. The predicted and ranked lncRNAs (excluding known correlated lncRNAs) correlated with 226 diseases on Dataset 3 can be seen in via genistein, and the expression of HOTAIR in castration-resistant PCa cell line is higher than that of standard prostate cell lines  and the Natural Science Foundation of Hunan, China (Grant No. 2018JJ2461).JY and GT were employed by company Geneis Beijing Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflict of interest.


Over the past decades, a large number of long non-coding RNAs (lncRNAs) have been identified. Growing evidence has indicated that the mutation and dysregulation of lncRNAs play a critical role in the development of many complex human diseases. Consequently, identifying potential disease-related lncRNAs is an effective means to improve the quality of disease diagnostics and treatment, which is the motivation of this work. Here, we propose a computational model (LncDisAP) for potential disease-related lncRNA identification based on multiple biological datasets. First, the associations between lncRNA and different data sources are collected from different databases. With these data sources as dimensions, we calculate the functional associations between lncRNAs by the recommendation strategy of collaborative filtering. Subsequently, a disease-associated lncRNA functional network is built with functional similarities between lncRNAs as the weight. Ultimately, potential disease-related lncRNAs can be identified based on ranked scores derived by random walking with restart (RWR). Then, training sets and testing sets are extracted from two different versions of a disease-lncRNA dataset to assess the performance of LncDisAP on 54 diseases.A lncRNA functional network is built based on the proposed computational model, and it contains 66,060 associations among 364 lncRNAs associated with 182 diseases in total. We extract 218 known disease-lncRNA pairs associated with 54 diseases to assess the network. As a result, the average AUC (area under the receiver operating characteristic curve) of LncDisAP is 78.08%.In this article, a computational model integrating multiple lncRNA-related biological datasets is proposed for identifying potential disease-related lncRNAs. The result shows that LncDisAP is successful in predicting novel disease-related lncRNA signatures. In addition, with several common cancers taken as case studies, we found some unknown lncRNAs that could be associated with these diseases through our network. These results suggest that this method can be helpful in improving the quality for disease diagnostics and treatment. Long non-coding RNAs (lncRNAs), which compose the largest portion of the mammalian non-coding transcriptome , are emeLncRNAs are the key to explaining disease mechanisms. As analysing lncRNAs is very appealing to researchers, many researchers have devoted their work to lncRNAs for exploring complex human diseases at the molecular level. For example, BCYRN1 has been demonstrated to induce the proliferation and migration of non-small cell lung cancer (NSCLC) cells and play an important role in NSCLC progression . LncRNA Although a large number of lncRNAs have been recorded in public databases, such as GENCODE , NONCODEThe workflow of LncDisAP is shown in Fig. DO  databaseRNAcentral  is a datLncRNADisease  is a datSTRING  is a datstarBase v2.0  systematThe differences in different data sets bring some difficulties to the integration of lncRNA data. Two problems must be solved before constructing the lncRNA functional association network. One is the mapping of disease terms. MEDIC and DO are both comprehensive disease corpuses and contain abundant disease terms, so we can annotate DO entries with the vocabulary from MEDIC and create a combined vocabulary of disease terms. Referring to this vocabulary, we build mappings between the DO terms and the disease terms of LncRNADisease. The other problem that must be addressed is the unification of lncRNA identifications. As mentioned above, the lncRNA naming rules of different lncRNA databases are different. Therefore, we employ the RNAcentral id as the unified identification system of lncRNAs considering that the RNAcentral database provides mapping data among various public lncRNA databases.da and db is defined as follows:Ga = {ga1, ga2, …} and Gb = {gb1, gb2, …} are related to disease da and db, respectively; num(G) represents the numbers of genes related to one disease; and RG(g) represents the degree of association between a gene g and a gene set G . ConsidSTRING provides human protein-protein interactions, and in the above section, the functional similarities between lncRNA-related diseases have been calculated. Therefore, we can obtain the relational degrees between one lncRNA and a certain disease or protein based on the similarities of lncRNA-related diseases or proteins. Then, these degrees can be used to make a multi-dimensional vector for each lncRNA. Hence, we can calculate lncRNA functional similarity by cosine similarity in a multi-dimensional space, which is defined by lncRNA-related diseases and proteins. The workflow of calculating lncRNA functional similarity based on the recommendation strategy of collaborative filtering is shown in Fig. L as the set of lncRNAs, D as the set of lncRNA-related diseases and P as the set of lncRNA-related proteins. DRl is defined as the set of diseases directly related to lncRNA l. The predicted association score between disease d and lncRNA l is defined as follows:l ∈ L, d ∈ D, DRl ⊆ D and 1 ≤ i ≤ |DRl|; here, |DRl| represents the number of diseases in the set of DRl. Similarly, for lncRNA-related proteins, PRl is defined as the set of proteins directly related to lncRNA l. The predicted association score between protein p and lncRNA l is defined as follows:l ∈ L, p ∈ P, PRl ⊆ P and 1 ≤ i ≤ |PRl|; here, |PRl| represents the number of proteins in the set of PRl and SPscore  represents the relevance score between protein p and pi from STRING.In this multi-dimensional space, neither all diseases nor all proteins are directly related to one lncRNA. To predict the score of a disease that is not directly related to one lncRNA, we define D| + |P| dimensions. |D| and |P| represent the size of the disease set D and the protein set P, respectively. For each lncRNA, we can define its vector l in this multi-dimensional space. AS and AS are the scores of disease dk and protein pj, respectively, for lncRNA l. Now, we can obtain |L| vectors of lncRNAs.Subsequently, we define a vector of each lncRNA with |l1 and lncRNA l2 is defined as follows:ASk,i represents the association score in the i-th dimension of the vector lk. The range of CR is 0 to 1 because these values of ASk,i are positive numbers.In this multi-dimensional space, each lncRNA can be depicted by a multi-dimensional vector. Therefore, we can measure the similarity between any two vectors of lncRNAs based on cosine similarity. The similarity between lncRNA mRNALinkSet. First, the relevance between an mRNA k and an mRNA set M is defined as follows:links represents the number of links between mRNA k and members in the mRNA set M, and these links have to be included in mRNALinkSet. Let a pair of mRNA sets M1 = {m11, m12, …} and M2 = {m21, m22, …} be related to lncRNA l1 and l2, respectively. The similarity between lncRNA l1 and l2 based on mRNA is defined as follows:M1| and |M2| represent the numbers of mRNAs related to lncRNA l1 and l2, respectively. Finally, we complete the calculation of lncRNA similarities based on different lncRNA-related knowledge.In addition, mRNA can also be seen as a factor to calculate lncRNA functional similarity because of the existing links between lncRNAs and mRNAs. In view of the relationships between mRNAs, we can extract links between them from HPRD denoted as l1 and l2 is defined as follows:LncFunNet is 0 to 1, as in CR and MR. Utilizing this lncRNA network, we can identify novel candidate disease-related lncRNAs.We can take lncRNA similarities as weight to construct a lncRNA functional association network. In this network, the weight between lncRNA To identify novel candidate disease-related lncRNAs, we employ RWR to fully exploit the global functional associations between lncRNAs in this network. RWR, as a global optimization method, can reveal more information between one lncRNA and all the others in the network. The random walker in the network starts from the root node and moves to adjacent nodes with the probabilities from that node to the others. After enough iterations, the probabilities from the root node to all the other nodes will become stable, which can be used as scores for predicting novel disease-related lncRNAs . There were 5,600,133 relationships between 13,716 mRNA and 1034 lncRNAs extracted from starBase v2.0. We found 15,622 associations between 33 proteins and 2750 lncRNAs from starBase v2.0 and STRING.We calculated similarity among 374 lncRNAs and removed lncRNA pairs that had a similarity of 0. Finally, we built a lncRNA functional network, which contains 66,060 associations among 364 lncRNAs associated with 182 diseases.To assess the performance of the lncRNA functional network, we compared two different versions of LncRNADisease and extracted 218 known disease-lncRNA pairs associated with 54 diseases from the newer version of LncRNADisease (released in June 2018). The detailed statistics for evaluating disease-related lncRNA networks are given in Additional file As a result, the disease-related lncRNA functional network has a good performance in predicting disease-lncRNA pairs for the 54 diseases with an average AUC value of 78.08%. The performance in predicting lncRNAs associated with cholangiocarcinoma is shown in Fig. Many studies have indicated that lncRNAs play critical roles in the development of various cancers . To furtThe relationship between diseases and lncRNAs in the data source was extracted from LncRNADisease released in July 2017. Hence, we evaluated the impact of different data sources and different test sets on the performance of predicting disease-lncRNA pairs in the disease-related lncRNA functional network. First, with the LncRNADisease data set released in June 2018 as the test set, we compared the two lncRNA functional networks that were built based on data sources from the 2015 and 2017 versions. After the abovementioned validation strategy was carried out, the lncRNA functional network based on the data source from LncRNADisease released in 2015 had an AUC value of 72.6%, while the AUC of the network based on the 2017 version reached 78.08%. Simultaneously, we assessed the performance of the lncRNA functional network based on the data source of the 2015 version with a test set extracted from the 2017 version, whose AUC reached 72.8%, as shown in Fig. l1 and l2, denoted as ER. The similarity between lncRNA l1 and l2 is defined as follows:The introduction of lncRNA expression similarity has been considered before. However, the results are not ideal. We obtained lncRNA expression profiles from NONCODE . This daSubsequently, we built two lncRNA functional networks based on data sources from the 2015 and 2017 versions with the lncRNA expression similarity operator introduced. LncRNADisease released in June 2018 was taken as the test set. The lncRNA functional network based on LncRNADisease released in 2015 had an AUC value of 68.3%, while the AUC of the network based on the 2017 version achieved 75.46%. As shown in Fig. In this article, a computational model for potential disease-related lncRNA identification was proposed based on multiple biological datasets. The results showed that LncDisAP was proven to be successful in predicting novel disease-related lncRNA signatures with an average AUC value of 78.08% and can be an effective solution to improve the quality of disease diagnostics and treatments. To further evaluate the performance of our computational model, we used several common cancers as case studies. We found some unknown lncRNAs that could be associated with these diseases through our network. In addition, we discussed the impact of different data sources and different test sets on the performance of the disease-related lncRNA functional network in predicting disease-lncRNA pairs.Additional file 1: Statistics of 54 diseases for evaluating disease-related lncRNA functional network.