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      A new computational drug repurposing method using established disease-drug pair knowledge.

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          Abstract

          Drug repurposing is a potential alternative to the classical drug discovery pipeline. Repurposing involves finding novel indications for already approved drugs. In this work, we present a novel machine learning-based method for drug repurposing. This method explores the anti-similarity between drugs and a disease to uncover new uses for the drugs. More specifically, our proposed method takes into account three sources of information: (i) large-scale gene expression profiles corresponding to human cell lines treated with small molecules, (ii) gene expression profile of a human disease and (iii) the known relationship between Food and Drug Administration (FDA)-approved drugs and diseases. Using these data, our proposed method learns a similarity metric through a supervised machine learning-based algorithm such that a disease and its associated FDA-approved drugs have smaller distance than the other disease-drug pairs.

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          Author and article information

          Journal
          Bioinformatics
          Bioinformatics (Oxford, England)
          Oxford University Press (OUP)
          1367-4811
          1367-4803
          Oct 01 2019
          : 35
          : 19
          Affiliations
          [1 ] Department of Computer Science, Wayne State University, Detroit, MI, USA.
          [2 ] Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI, USA.
          Article
          5370176
          10.1093/bioinformatics/btz156
          6761937
          30840053
          d6ef6f61-ca7a-4c77-9494-002f2c05786c
          © The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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