Using Drug Expression Profiles and Machine Learning Approach for Drug Repurposing.
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香港中文大學研究人員

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摘要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.
著者Kai ZHAO, Hon-Cheong SO
出版作品名稱Computational Methods for Drug Repurposing
詳細描述Part of the Methods in Molecular Biology book series (MIMB, volume 1903)
出版年份2018
月份12
日期14
卷號1903
頁次219 - 237
國際標準期刊號1064-3745
語言英式英語

上次更新時間 2021-02-12 於 00:39