Predicting lncRNA-disease associations using network topological similarity based on deep mining heterogeneous networks.

Predicting lncRNA-disease associations using network topological similarity based on deep mining heterogeneous networks. Math Biosci. 2019 Jul 16;:108229 Authors: Zhang H, Liang Y, Peng C, Han S, Du W, Li Y Abstract A kind of noncoding RNA with length more than 200 nucleotides named long noncoding RNA (lncRNA) has gained considerable attention in recent decades. Many studies have confirmed that human genome contains many thousands of lncRNAs. LncRNAs play significant roles in many important biological processes, including complex disease diagnosis, prognosis, prevention and treatment. For some important diseases such as cancer, lncRNAs have been novel candidate biomarkers. However, the role of lncRNAs in human diseases is still in its infancy, and only a small part of lncRNA-disease associations have been experimentally verified. Predicting lncRNA-disease association is an important way to understand the mechanism and function of lncRNA involved in diseases to enrich the annotations of lncRNA. Therefore, it is urgent to prioritize lncRNAs potentially associated with diseases. Biological system is a highly complex heterogenous network involved different molecules. Therefore, the algorithms based on network methods have been extensively applied in information fields which can provide a quantifiable characterization for the networks characterizing multifarious biological systems. A heterogeneous network topology possessing abundant inte...
Source: Mathematical Biosciences - Category: Statistics Authors: Tags: Math Biosci Source Type: research