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Using Drug Expression Profiles and Machine Learning Approach for Drug Repurposing

  • Kai Zhao
  • Hon-Cheong SoEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1903)

Abstract

The cost of new drug development has been increasing, and repurposing known medications for new indications serves as an important way to hasten drug discovery. One promising approach to drug repositioning is to take advantage of machine learning (ML) algorithms to learn patterns in biological data related to drugs and then link them up to the potential of treating specific diseases. Here we give an overview of the general principles and different types of ML algorithms, as well as common approaches to evaluating predictive performances, with reference to the application of ML algorithms to predict repurposing opportunities using drug expression data as features. We will highlight common issues and caveats when applying such models to repositioning. We also introduce resources of drug expression data and highlight recent studies employing such an approach to repositioning.

Key words

Drug repositioning Machine learning Drug transcriptome Genomics Deep learning 

Notes

Acknowledgment

This work is partially supported by the Lo Kwee-Seong Biomedical Research Fund and a Direct Grant from the Chinese University of Hong Kong to HCS.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Biomedical SciencesThe Chinese University of Hong KongShatinHong Kong
  2. 2.KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common DiseasesKunming Zoology Institute of ZoologyKunmingChina

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