A Novel Method for LncRNA-Disease Association Prediction Based on an lncRNA-Disease Association Network

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 16; no. 2; pp. 688 - 693
• Main Authors: Pengyao Ping, Lei Wang, Linai Kuang, Songtao Ye, Iqbal, Muhammad Faisal Buland, Tingrui Pei
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Biological activity; Biomarkers; bipartite network; Cancer; Computational Biology - methods; computational model; Computational modeling; Computer applications; Computer simulation; Data bases; Databases, Genetic; Humans; LncRNA-disease associations; Mathematical models; Medical treatment; Models, Genetic; Models, Statistical; Molecular modelling; Neoplasms - diagnosis; Neoplasms - genetics; Predictions; Predictive models; Prognosis; RNA; RNA, Long Noncoding - genetics
• ISSN: 1545-5963; 1557-9964
• DOI: 10.1109/TCBB.2018.2827373

An increasing number of studies have indicated that long-non-coding RNAs (lncRNAs) play critical roles in many important biological processes. Predicting potential lncRNA-disease associations can improve our understanding of the molecular mechanisms of human diseases and aid in finding biomarkers for disease diagnosis, treatment, and prevention. In this paper, we constructed a bipartite network based on known lncRNA-disease associations; based on this work, we proposed a novel model for inferring potential lncRNA-disease associations. Specifically, we analyzed the properties of the bipartite network and found that it closely followed a power-law distribution. Moreover, to evaluate the performance of our model, a leave-one-out cross-validation (LOOCV) framework was implemented, and the simulation results showed that our computational model significantly outperformed previous state-of-the-art models, with AUCs of 0.8825, 0.9004, and 0.9292 for known lncRNA-disease associations obtained from the LncRNADisease database, Lnc2Cancer database, and MNDR database, respectively. Thus, our approach may be an excellent addition to the biomedical research field in the future.