Prediction of miRNA-disease Associations using an Evolutionary Tuned Latent Semantic Analysis

Abstract : MicroRNAs, small non-coding elements implied in gene regulation, are very interesting biomarkers for various diseases such as cancers. They represent potential prodigious biotechnologies for early diagnosis and gene therapies. However, experimental verification of microRNA-disease associations are time-consuming and costly, so that computational modeling is a proper solution. Previously, we designed MiRAI, a predictive method based on distributional semantics, to identify new associations between microRNA molecules and human diseases. Our preliminary results showed very good prediction scores compared to other available methods. However, MiRAI performances depend on numerous parameters that cannot be tuned manually. In this study, a parallel evolutionary algorithm is proposed for finding an optimal configuration of our predictive method. The automatically parametrized version of MiRAI achieved excellent performance. It highlighted new miRNA-disease associations, especially the potential implication of mir-188 and mir-795 in various diseases. In addition, our method allowed to detect several putative false associations contained in the reference database.
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Denis Pallez, Julien Gardès, Claude Pasquier. Prediction of miRNA-disease Associations using an Evolutionary Tuned Latent Semantic Analysis. Scientific Reports, Nature Publishing Group, 2017, 7, pp.10548. 〈10.1038/s41598-017-10065-y〉. 〈hal-01582170〉

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