LiDiAimc: LincRNA-disease associations through inductive matrix completion

• Published in: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) pp. 158 - 163
• Main Authors: Biswas, Ashis, Jean Gao
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2017
• Subjects: Association inference; Bioinformatics; Diseases; Inductive Matrix Completion; Long intergenic non-coding RNA; OMIM disease phenotypes; Predictive models; Principal component analysis; RNA
• DOI: 10.1109/BIBM.2017.8217643

The dysregulations of long intergenic non-coding RNAs (lincRNAs) have shown to be linked with a wide variety of human diseases over the past few years. However, there are only a few lincRNA-disease association inference tools available with most of them relying on very specific type of prior knowledge about the lincRNAs and the diseases. They fall short in generalized association predictions when new type of knowledge becomes available, or to rank disease implications by a novel lincRNA. In this article, we proposed LiDiAimc, a method based on Inductive Matrix Completion strategy, offering generalized integration platform of any type of prior knowledge about the lincRNAs and the diseases. A benefit of the approach is that being an inductive learner, it can be applied to lincRNAs for disease association predictions that are not listed during model build-up phase, and vice versa, an approach unlike traditional matrix factorization frameworks. The proposed LiDiAimc method was applied to association data between human lincRNAs and OMIM disease phenotypes as well as a diverse set of knowledgebase of lincRNAs and diseases. Our method performs better than the state-of-the-art methods in terms of precision@k and recall@k at the top-k disease prioritization to the subject lincRNAs.