Constructing lncRNA functional similarity network based on lncRNA-disease associations and disease semantic similarity

• Published in: Scientific reports Vol. 5; no. 1; p. 11338
• Main Authors: Chen, Xing, Clarence Yan, Chenggang, Luo, Cai, Ji, Wen, Zhang, Yongdong, Dai, Qionghai
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 10.06.2015; Nature Publishing Group
• Subjects: 631/114/2397; 631/337/384/2568; 631/553/2695; Colorectal cancer; Colorectal carcinoma; Colorectal Neoplasms - genetics; Computational Biology; Computer applications; Computer Simulation; Female; Gene Library; Gene Regulatory Networks - genetics; Genetic Predisposition to Disease; Humanities and Social Sciences; Humans; Lung cancer; Lung Neoplasms - genetics; Male; Mathematical models; Models, Biological; multidisciplinary; Prediction models; Preschool children; RNA, Long Noncoding - genetics; Science
• DOI: 10.1038/srep11338
• ISSN: 2045-2322

Increasing evidence has indicated that plenty of lncRNAs play important roles in many critical biological processes. Developing powerful computational models to construct lncRNA functional similarity network based on heterogeneous biological datasets is one of the most important and popular topics in the fields of both lncRNAs and complex diseases. Functional similarity network consturction could benefit the model development for both lncRNA function inference and lncRNA-disease association identification. However, little effort has been attempted to analysis and calculate lncRNA functional similarity on a large scale. In this study, based on the assumption that functionally similar lncRNAs tend to be associated with similar diseases, we developed two novel lncRNA functional similarity calculation models (LNCSIM). LNCSIM was evaluated by introducing similarity scores into the model of Laplacian Regularized Least Squares for LncRNA–Disease Association (LRLSLDA) for lncRNA-disease association prediction. As a result, new predictive models improved the performance of LRLSLDA in the leave-one-out cross validation of various known lncRNA-disease associations datasets. Furthermore, some of the predictive results for colorectal cancer and lung cancer were verified by independent biological experimental studies. It is anticipated that LNCSIM could be a useful and important biological tool for human disease diagnosis, treatment and prevention.