Prediction of miRNA-disease Associations using an Evolutionary Tuned Latent Semantic Analysis.
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ABSTRACT:
SUBMITTER: Pallez D
PROVIDER: S-EPMC5585369 | biostudies-literature | 2017 Sep
REPOSITORIES: biostudies-literature
Publications
Prediction of miRNA-disease Associations using an Evolutionary Tuned Latent Semantic Analysis.
Pallez Denis D Gardès Julien J Pasquier Claude C
Scientific reports 20170905 1
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 betwee ...[more]
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Project description:BackgroundIt has been shown that the deregulation of miRNAs is associated with the development and progression of many human diseases. To reduce time and cost of biological experiments, a number of algorithms have been proposed for predicting miRNA-disease associations. However, the existing methods rarely investigated the cause-and-effect mechanism behind these associations, which hindered further biomedical follow-ups.ResultsIn this study, we presented a CCA-based model in which the possible molecular causes of miRNA-disease associations were comprehensively revealed by extracting correlated sets of genes and diseases based on the co-occurrence of miRNAs in target gene profiles and disease profiles. Our method directly suggested the underlying genes involved, which could be used for experimental tests and confirmation. The inference of associated diseases of a new miRNA was made by taking into account the weight vectors of the extracted sets. We extracted 60 pairs of correlated sets from 404 miRNAs with two profiles for 2796 target genes and 362 diseases. The extracted diseases could be considered as possible outcomes of miRNAs regulating the target genes which appeared in the same set, some of which were supported by independent source of information. Furthermore, we tested our method on the 404 miRNAs under the condition of 5-fold cross validations and received an AUC value of 0.84606. Finally, we extensively inferred miRNA-disease associations for 100 new miRNAs and some interesting prediction results were validated by established databases.ConclusionsThe encouraging results demonstrated that our method could provide a biologically relevant prediction and interpretation of associations between miRNAs and diseases, which were of great usefulness when guiding biological experiments for scientific research.
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| S-EPMC9992841 | biostudies-literature