LDGRNMF: LncRNA-disease associations prediction based on graph regularized non-negative matrix factorization

Abstract

Emerging evidence suggests that long non-coding RNAs (lncRNAs) play an important role in various biological processes and human diseases. Exploring the associations between lncRNAs and diseases can better understand the complex disease mechanisms. However, expensive and time-consuming for exploring by biological experiments, it is imperative to develop more accurate and efficient computational approaches to predicting lncRNA-disease associations. In this work, we develop a new computational approach to predict lncRNA-disease associations using graph regularized nonnegative matrix factorization (LDGRNMF), which considers disease-associated lncRNAs identification as recommendation system problem. More specifically, we calculate the similarity of disease based on Gaussian interaction profile kernel and disease semantic information, and calculate the similarity of lncRNA based on Gaussian interaction profile kernel. Secondly, the weighted K nearest known neighbor interaction profiles is applied to reconstruct lncRNA-disease association adjacency matrix. Finally, graph regularized nonnegative matrix factorization is exploited to predict the potential associations between lncRNAs and diseases. In the five-fold cross-validation experiments, LDGRNMF achieves AUC of 0.8985 which outperforms other compared methods. Moreover, in case studies for stomach cancer, breast cancer and lung cancer, 9, 8 and 6 of the top 10 candidate lncRNAs predicted by LDGRNMF are verified, respectively. Rigorous experimental results indicate that our method can be regarded as an effectively tool for predicting potential lncRNA-disease associations.