Overcoming selection bias in synthetic lethality prediction.

ABSTRACT:

Motivation

Synthetic lethality (SL) between two genes occurs when simultaneous loss of function leads to cell death. This holds great promise for developing anti-cancer therapeutics that target synthetic lethal pairs of endogenously disrupted genes. Identifying novel SL relationships through exhaustive experimental screens is challenging, due to the vast number of candidate pairs. Computational SL prediction is therefore sought to identify promising SL gene pairs for further experimentation. However, current SL prediction methods lack consideration for generalizability in the presence of selection bias in SL data.

Results

We show that SL data exhibit considerable gene selection bias. Our experiments designed to assess the robustness of SL prediction reveal that models driven

SUBMITTER: Seale C 

PROVIDER: S-EPMC9477536 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature