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Prediction Of Lncrna-Disease Associations Based On Inductive Matrix Completion, Chengqian Lu, Mengyun Yang, Feng Luo, Fang-Xiang Wu, Min Li, Yi Pan, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation: Accumulating evidences indicate that long non-coding RNAs (lncRNAs) play pivotal roles in various biological processes. Mutations and dysregulations of lncRNAs are implicated in miscellaneous human diseases. Predicting lncRNA–disease associations is beneficial to disease diagnosis as well as treatment. Although many computational methods have been developed, precisely identifying lncRNA–disease associations, especially for novel lncRNAs, remains challenging.

Results: In this study, we propose a method (named SIMCLDA) for predicting potential lncRNA– disease associations based on inductive matrix completion. We compute Gaussian interaction profile kernel of lncRNAs from known lncRNA–disease interactions and functional similarity of diseases based on disease–gene and gene–gene onotology …