TAIJI: Approaching Experimental Replicates-Level Accuracy for Drug Synergy Prediction.
Author: Li H1, Hu S1,2, Neamati N2, Guan Y1,3
Affiliation:
1Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, USA.
2Department of Medicinal Chemistry, College of Pharmacy, Rogel Cancer Center, University of Michigan, Ann Arbor, USA.
3Department of Internal Medicine, Nephrology Division, University of Michigan, Ann Arbor, USA.
Conference/Journal: Bioinformatics.
Date published: 2018 Nov 21
Other:
Special Notes: doi: 10.1093/bioinformatics/bty955. [Epub ahead of print] , Word Count: 184
Motivation: Combination therapy is widely used in cancer treatment to overcome drug resistance. High-throughput drug screening is the standard approach to study the drug combination effects, yet it becomes impractical when the number of drugs under consideration is large. Therefore, accurate and fast computational tools for predicting drug synergistic effects are needed to guide experimental design for developing candidate drug pairs.
Results: Here, we present TAIJI, a high-performance software for fast and accurate prediction of drug synergism. It is based on the winning algorithm in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge, which is a unique platform to unbiasedly evaluate the performance of current state-of-the-art methods, and includes 160 team-based submission methods. When tested across a broad spectrum of 85 different cancer cell lines and 1089 drug combinations, TAIJI achieved a high prediction correlation (0.53), approaching the accuracy level of experimental replicates (0.56). The runtime is at the scale of minutes to achieve this state-of-the-field performance.
Availability: TAIJI is freely available on GitHub (https://github.com/GuanLab/TAIJI). It is functional with built-in Perl and Python.
Supplementary information: Supplementary Data are available at Bioinformatics online.
PMID: 30462169 DOI: 10.1093/bioinformatics/bty955
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