한빛사논문
- 조회1,030
Joseph D. Janizek1,2, Ayse B. Dincer1, Safiye Celik3, Hugh Chen 1, William Chen1, Kamila Naxerova 4,5,6 & Su-In Lee 1,6
1Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA.
2Medical Scientist Training Program, University of Washington, Seattle, WA, USA.
3Recursion Pharmaceuticals, Salt Lake City, UT, USA.
4Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
5Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
6These authors contributed equally: Kamila Naxerova, Su-In Lee.
Corresponding authors : Correspondence to Kamila Naxerova or Su-In Lee.
Abstract
Machine learning may aid the choice of optimal combinations of anticancer drugs by explaining the molecular basis of their synergy. By combining accurate models with interpretable insights, explainable machine learning promises to accelerate data-driven cancer pharmacology. However, owing to the highly correlated and high-dimensional nature of transcriptomic data, naively applying current explainable machine-learning strategies to large transcriptomic datasets leads to suboptimal outcomes. Here by using feature attribution methods, we show that the quality of the explanations can be increased by leveraging ensembles of explainable machine-learning models. We applied the approach to a dataset of 133 combinations of 46 anticancer drugs tested in ex vivo tumour samples from 285 patients with acute myeloid leukaemia and uncovered a haematopoietic-differentiation signature underlying drug combinations with therapeutic synergy. Ensembles of machine-learning models trained to predict drug combination synergies on the basis of gene-expression data may improve the feature attribution quality of complex machine-learning models.
논문정보
- Research article
- 2023년 05월 (BRIC 등록일 2023-05-04)
- 국외 연구진
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