PREDICT: a method for inferring novel drug indications with application to personalized medicine

Molecular Systems Biology

Assaf Gottlieb²  Gideon Y Stein¹  Eytan Ruppin² 

[1] Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel;The Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel

关键词: drug indication prediction;    drug repositioning;    drug repurposing;    machine learning;    personalized medicine;   

DOI  :  10.1038/msb.2011.26

来源: Wiley

【 摘 要 】

Inferring potential drug indications, for either novel or approved drugs, is a key step in drug development. Previous computational methods in this domain have focused on either drug repositioning or matching drug and disease gene expression profiles. Here, we present a novel method for the large-scale prediction of drug indications (PREDICT) that can handle both approved drugs and novel molecules. Our method is based on the observation that similar drugs are indicated for similar diseases, and utilizes multiple drug–drug and disease–disease similarity measures for the prediction task. On cross-validation, it obtains high specificity and sensitivity (AUC=0.9) in predicting drug indications, surpassing existing methods. We validate our predictions by their overlap with drug i

【 授权许可】

CC BY-NC-SA   
Copyright © 2011 EMBO and Macmillan Publishers Limited

Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.