Title:SEMCM: A Self-Expressive Matrix Completion Model for Anti-cancer
Drug Sensitivity Prediction
Volume: 17
Issue: 5
Author(s): Lin Zhang, Yuwei Yuan, Jian Yu and Hui Liu*
Affiliation:
- Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, China
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
Keywords:
Anti-cancer sensitivity, response prediction, self-expressive, matrix completion, CCLE, GDSC.
Abstract:
Background: Genomic data sets generated by several recent large scale high-throughput
screening efforts pose a complex computational challenge for anticancer drug sensitivity prediction.
Objective: We aimed to design an algorithm model that would predict missing elements in incomplete
matrices and could be applicable to drug response prediction programs.
Methods: We developed a novel self-expressive matrix completion model to improve the predictive
performance of drug response prediction problems. The model is based on the idea of subspace clustering
and as a convex problem, it can be solved by alternating direction method of multipliers. The original
incomplete matrix can be filled through model training and parameters updated iteratively.
Results: We applied SEMCM to Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line
Encyclopedia (CCLE) datasets to predict unknown response values. A large number of experiments
have proved that the algorithm has good prediction results and stability, which are better than several
existing advanced drug sensitivity prediction and matrix completion algorithms. Without modeling mutation
information, SEMCM could correctly predict cell line-drug associations for mutated cell lines and
wild cell lines. SEMCM can also be used for drug repositioning. The newly predicted drug responses of
GDSC dataset suggest that TI-73 was sensitive to Erlotinib. Moreover, the sensitivity of A172 and NCIH1437
to Paclitaxel was roughly the same.
Conclusion: We report an efficient anticancer drug sensitivity prediction algorithm which is opensource
and can predict the unknown responses of cancer cell lines to drugs. Experimental results prove
that our method can not only improve the prediction accuracy but also can be applied to drug repositioning.
