Using Drug Expression Profiles and Machine Learning Approach for Drug Repurposing. View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2019

AUTHORS

Kai Zhao , Hon-Cheong So

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. More... »

PAGES

219-237

References to SciGraph publications

Book

TITLE

Computational Methods for Drug Repurposing

ISBN

978-1-4939-8954-6
978-1-4939-8955-3

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-1-4939-8955-3_13

DOI

http://dx.doi.org/10.1007/978-1-4939-8955-3_13

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1110579138

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30547445


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