Motivation: More and more evidences have indicated that long–non-coding RNAs (lncRNAs) play critical roles in many important biological processes. Therefore, mutations and dysregulations of these lncRNAs would contribute to the development of various complex diseases. Developing powerful computational models for potential disease-related lncRNAs identification would benefit biomarker identification and drug discovery for human disease diagnosis, treatment, prognosis and prevention. Results: In this article, we proposed the assumption that similar diseases tend to be associated with functionally similar lncRNAs. Then, we further developed the method of Laplacian Regularized Least Squares for LncRNA–Disease Association (LRLSLDA) in the semisupervised learning framework. Although known disease–lncRNA associations in the database are rare, LRLSLDA still obtained an AUC of 0.7760 in the leave-one-out cross validation, significantly improving the performance of previous methods. We also illustrated the performance of LRLSLDA is not sensitive (even robust) to the parameters selection and it can obtain a reliable performance in all the test classes. Plenty of potential disease–lncRNA associations were publicly released and some of them have been confirmed by recent results in biological experiments. It is anticipated that LRLSLDA could be an effective and important biological tool for biomedical research. Availability: The code of LRLSLDA is freely available at http://asdcd.amss.ac.cn/Software/Details/2. Contact:xingchen@amss.ac.cn or yangy@amt.ac.cn Supplementary information: Supplementary data are available at Bioinformatics online.

Plenty of microRNAs (miRNAs) were discovered at a rapid pace in plants, green algae, viruses and animals. As one of the most important components in the cell, miRNAs play a growing important role in various essential and important biological processes. For the recent few decades, amounts of experimental methods and computational models have been designed and implemented to identify novel miRNA–disease associations. In this review, the functions of miRNAs, miRNA–target interactions, miRNA–disease associations and some important publicly available miRNA-related databases were discussed in detail. Specially, considering the important fact that an increasing number of miRNA–disease associations have been experimentally confirmed, we selected five important miRNA-related human diseases and five crucial disease-related miRNAs and provided corresponding introductions. Identifying disease-related miRNAs has become an important goal of biomedical research, which will accelerate the understanding of disease pathogenesis at the molecular level and molecular tools design for disease diagnosis, treatment and prevention. Computational models have become an important means for novel miRNA–disease association identification, which could select the most promising miRNA–disease pairs for experimental validation and significantly reduce the time and cost of the biological experiments. Here, we reviewed 20 state-of-the-art computational models of predicting miRNA–disease associations from different perspectives. Finally, we summarized four important factors for the difficulties of predicting potential disease-related miRNAs, the framework of constructing powerful computational models to predict potential miRNA–disease associations including five feasible and important research schemas, and future directions for further development of computational models.

Predicting potential drug–target interactions from heterogeneous biological data is critical not only for better understanding of the various interactions and biological processes, but also for the development of novel drugs and the improvement of human medicines. In this paper, the method of Network-based Random Walk with Restart on the Heterogeneous network (NRWRH) is developed to predict potential drug–target interactions on a large scale under the hypothesis that similar drugs often target similar target proteins and the framework of Random Walk. Compared with traditional supervised or semi-supervised methods, NRWRH makes full use of the tool of the network for data integration to predict drug–target associations. It integrates three different networks (protein–protein similarity network, drug–drug similarity network, and known drug–target interaction networks) into a heterogeneous network by known drug–target interactions and implements the random walk on this heterogeneous network. When applied to four classes of important drug–target interactions including enzymes, ion channels, GPCRs and nuclear receptors, NRWRH significantly improves previous methods in terms of cross-validation and potential drug–target interaction prediction. Excellent performance enables us to suggest a number of new potential drug–target interactions for drug development.

It has been shown that microRNAs (miRNAs) play key roles in variety of biological processes associated with human diseases. In Consideration of the cost and complexity of biological experiments, computational methods for predicting potential associations between miRNAs and diseases would be an effective complement. This paper presents a novel model of Inductive Matrix Completion for MiRNA–Disease Association prediction (IMCMDA). The integrated miRNA similarity and disease similarity are calculated based on miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity. The main idea is to complete the missing miRNA–disease association based on the known associations and the integrated miRNA similarity and disease similarity. IMCMDA achieves AUC of 0.8034 based on leave-one-out-cross-validation and improved previous models. In addition, IMCMDA was applied to five common human diseases in three types of case studies. In the first type, respectively, 42, 44, 45 out of top 50 predicted miRNAs of Colon Neoplasms, Kidney Neoplasms, Lymphoma were confirmed by experimental reports. In the second type of case study for new diseases without any known miRNAs, we chose Breast Neoplasms as the test example by hiding the association information between the miRNAs and Breast Neoplasms. As a result, 50 out of top 50 predicted Breast Neoplasms-related miRNAs are verified. In the third type of case study, IMCMDA was tested on HMDD V1.0 to assess the robustness of IMCMDA, 49 out of top 50 predicted Esophageal Neoplasms-related miRNAs are verified. The code and dataset of IMCMDA are freely available at https://github.com/IMCMDAsourcecode/IMCMDA. Supplementary data are available at Bioinformatics online.

LncRNAs have attracted lots of attentions from researchers worldwide in recent decades. With the rapid advances in both experimental technology and computational prediction algorithm, thousands of lncRNA have been identified in eukaryotic organisms ranging from nematodes to humans in the past few years. More and more research evidences have indicated that lncRNAs are involved in almost the whole life cycle of cells through different mechanisms and play important roles in many critical biological processes. Therefore, it is not surprising that the mutations and dysregulations of lncRNAs would contribute to the development of various human complex diseases. In this review, we first made a brief introduction about the functions of lncRNAs, five important lncRNA-related diseases, five critical disease-related lncRNAs and some important publicly available lncRNA-related databases about sequence, expression, function, etc. Nowadays, only a limited number of lncRNAs have been experimentally reported to be related to human diseases. Therefore, analyzing available lncRNA–disease associations and predicting potential human lncRNA–disease associations have become important tasks of bioinformatics, which would benefit human complex diseases mechanism understanding at lncRNA level, disease biomarker detection and disease diagnosis, treatment, prognosis and prevention. Furthermore, we introduced some state-of-the-art computational models, which could be effectively used to identify disease-related lncRNAs on a large scale and select the most promising disease-related lncRNAs for experimental validation. We also analyzed the limitations of these models and discussed the future directions of developing computational models for lncRNA research.

Recently, more and more research has shown that microRNAs (miRNAs) play critical roles in the development and progression of various diseases, but it is not easy to predict potential human miRNA–disease associations from the vast amount of biological data. Computational methods for predicting potential disease–miRNA associations have gained a lot of attention based on their feasibility, guidance and effectiveness. Differing from traditional local network similarity measures, we adopted global network similarity measures and developed Random Walk with Restart for MiRNA–Disease Association (RWRMDA) to infer potential miRNA–disease interactions by implementing random walk on the miRNA–miRNA functional similarity network. We tested RWRMDA on 1616 known miRNA–disease associations based on leave-one-out cross-validation, and achieved an area under the ROC curve of 86.17%, which significantly improves previous methods. The method was also applied to three cancers for accuracy evaluation. As a result, 98% (Breast cancer), 74% (Colon cancer), and 88% (Lung cancer) of top 50 predicted miRNAs are confirmed by published experiments. These results suggest that RWRMDA will represent an important bioinformatics resource in biomedical research of both miRNAs and diseases.