Benchmarking machine learning methods for synthetic lethality prediction in cancer
Yimiao Feng, Yahui Long, He Wang, Yang Ouyang, Quan Li, Min Wu, Jie Zheng
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. A comprehensive benchmarking of existing synthetic lethality (SL) prediction methods is lacking. Here, the authors compare 12 recently developed machine learning methods for SL prediction, assess their performance, and provide guidance on the selection of the most suitable method.