NMF-Based LncRNA-Disease Association Inference and Bi-Clustering

Long non-coding RNAs (lncRNAs) have been implicated in various biological processes, and are linked in many dysregulations. Researchers have reported large number of lncRNA associated human diseases over the past decade. In this article we employed the Non-negative Matrix Factorization method to develop a low-dimensional computational model that can describe the existing knowledge about lncRNA-disease associations represented in a two dimensional association matrix. The non-negativity constraints of the matrix and its corresponding factors ensure that each lncRNA´s disease profile can be represented as an additive linear combination of the latent coordinates. To learn such a constrained model from an incomplete association matrix, several NMF formulations were developed. Based on our experiments, we found that the Sparse NMF obtained the best model among all the other models. Moreover, by exploiting the inherent bi-clustering ability of the NMF models, we extracted several lncRNA groups and disease groups that possess biological significance.