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DRIMC: an improved drug repositioning approach using Bayesian inductive matrix completion.

07:00 EST 30th January 2020 | BioPortfolio

Summary of "DRIMC: an improved drug repositioning approach using Bayesian inductive matrix completion."

One of the most important problems in drug discovery research is to precisely predict a new indication for an existing drug, i.e. drug repositioning. Recent recommendation system-based methods have tackled this problem using matrix completion models. The models identify latent factors contributing to known drug-disease associations, and then infer novel drug-disease associations by the correlations between latent factors. However, these models have not fully considered the various drug data sources and the sparsity of the drug-disease association matrix. In addition, using the global structure of the drug-disease association data may introduce noise, and consequently limit the prediction power.

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Name: Bioinformatics (Oxford, England)
ISSN: 1367-4811
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