Inferring disease and pathway associations of long non-coding RNAs using heterogeneous information network model

Recent findings from biological experiments demonstrate that long non-coding RNAs (lncRNAs) are actively involved in critical cellular processes and are associated with innumerable diseases. Computational prediction of lncRNA-disease association draws tremendous research attention nowadays. This paper proposes a machine learning model that predicts lncRNA-disease associations using Heterogeneous Information Network (HIN) of lncRNAs and diseases. A Support Vector Machine classifier is developed using the feature set extracted from a meta-path-based parameter, Association Index derived from the HIN. Performance of the model is validated using standard statistical metrics and it generated an AUC value of 0.87, which is better than the existing methods in the literature. Results are further validated using the recent literature and many of the predicted lncRNA-disease associations are identified as actually existing. This paper also proposes an HIN-based methodology to associate lncRNAs with pathways in which they may have biological influence. A case study on the pathway associations of four well-known lncRNAs (HOTAIR, TUG1, NEAT1, and MALAT1) has been conducted. It has been observed that many times the same lncRNA is associated with more than one biologically related pathways. Further exploration is needed to substantiate whether such lncRNAs have any role in determining the pathway interplay. The script and sample data for the model construction is freely available at http://bdbl.nitc.ac.in/LncDisPath/index.html.