A Network-Driven Approach for LncRNA-Disease Association Mapping.

Aberrant lncRNA may contributes to development of cancer. It remains, however, a challenge to understand associations between lncRNA and cancer because of complex mechanisms involved in associations and insufficient sample sizes. With unprecedented wealth of lncRNA data, gene expression data and disease status data give us a new opportunity to design machine learning method to investigate underlying association mechanisms. In this paper, we propose a network-driven approach named NLDAM which for lncRNA-disease association mapping. NLDAM detects associations between lncRNA and genes, genes and disease. NLDAM constructs an association network, where nodes represent lncRNA, genes or disease status, and weighted edges represent significance of associations between nodes. NLDAM identifies significant paths from lncRNA to disease based on the weighted scores. The experimental results on synthetic datasets show the advantage of NLDAM in terms of lncRNA selection accuracy than traditional methylation sites search methods (including NTSDMHN and IDHI-MIRW) under false positive control. Furthermore, we applied NLDAM on ovarian cancer data from The Cancer Genome Atlas database and identified significant lncRNA-gene-disease path associations, among which we analyzed top 10 paths associated with oncogenes. We also provide hypothetical biological path associations to explain our findings.