IDHI-MIRW – prediction of lncRNA-disease associations by integrating diverse heterogeneous information sources

Long non-coding RNAs play an important role in human complex diseases. Identification of lncRNA-disease associations will gain insight into disease-related lncRNAs and benefit disease diagnoses and treatment. However, using experiments to explore the lncRNA-disease associations is expensive and time consuming.

In this study, researchers at Northwestern Polytechnical University and the University of Pittsburgh developed a novel method to identify potential lncRNA-disease associations by Integrating Diverse Heterogeneous Information sources with positive pointwise Mutual Information and Random Walk with restart algorithm (namely IDHI-MIRW). IDHI-MIRW first constructs multiple lncRNA similarity networks and disease similarity networks from diverse lncRNA-related and disease-related datasets, then implements the random walk with restart algorithm on these similarity networks for extracting the topological similarities which are fused with positive pointwise mutual information to build a large-scale lncRNA-disease heterogeneous network. Finally, IDHI-MIRW implemented random walk with restart algorithm on the lncRNA-disease heterogeneous network to infer potential lncRNA-disease associations.

Flowchart of the IDHI-MIRW

lncRNA

a building three lncRNA similarity networks and three disease similarity networks by calculating the Pearson correlation coefficient and Gaussian interaction profile kernel similarity. b forming the lncRNA/disease topological similarity networks with RWR and positive pointwise mutual information. c constructing the large-scale lncRNA-disease heterogeneous network by integrating lncRNA/disease topological similarities and known lncRNA-disease associations. d predicting the potential lncRNA-disease associations by implementing RWRH

Compared with other state-of-the-art methods, IDHI-MIRW achieves the best prediction performance. In case studies of breast cancer, stomach cancer, and colorectal cancer, 36/45 (80%) novel lncRNA-disease associations predicted by IDHI-MIRW are supported by recent literatures. Furthermore, we found lncRNA LINC01816 is associated with the survival of colorectal cancer patients.

Availability –  IDHI-MIRW is freely available at https://github.com/NWPU-903PR/IDHI-MIRW .

Fan XN, Zhang SW, Zhang SY, Zhu K, Lu S. (2019) Prediction of lncRNA-disease associations by integrating diverse heterogeneous information sources with RWR algorithm and positive pointwise mutual information. BMC Bioinformatics 20(1):87. [article]

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