Benchmarking machine learning methods for synthetic lethality prediction in cancer

Yimiao Feng, Yahui Long, He Wang, Yang Ouyang, Quan Li, Min Wu () and Jie Zheng ()
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Yimiao Feng: ShanghaiTech University
Yahui Long: Agency for Science, Technology and Research (A*STAR)
He Wang: ShanghaiTech University
Yang Ouyang: ShanghaiTech University
Quan Li: ShanghaiTech University
Min Wu: Agency for Science, Technology and Research (A*STAR)
Jie Zheng: ShanghaiTech University

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract Synthetic lethality (SL) is a gold mine of anticancer drug targets, exposing cancer-specific dependencies of cellular survival. To complement resource-intensive experimental screening, many machine learning methods for SL prediction have emerged recently. However, a comprehensive benchmarking is lacking. This study systematically benchmarks 12 recent machine learning methods for SL prediction, assessing their performance across diverse data splitting scenarios, negative sample ratios, and negative sampling techniques, on both classification and ranking tasks. We observe that all the methods can perform significantly better by improving data quality, e.g., excluding computationally derived SLs from training and sampling negative labels based on gene expression. Among the methods, SLMGAE performs the best. Furthermore, the methods have limitations in realistic scenarios such as cold-start independent tests and context-specific SLs. These results, together with source code and datasets made freely available, provide guidance for selecting suitable methods and developing more powerful techniques for SL virtual screening.

Date: 2024
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DOI: 10.1038/s41467-024-52900-7

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