ELISL: early-late integrated synthetic lethality prediction in cancer.

ABSTRACT:

Motivation

Anti-cancer therapies based on synthetic lethality (SL) exploit tumour vulnerabilities for treatment with reduced side effects, by targeting a gene that is jointly essential with another whose function is lost. Computational prediction is key to expedite SL screening, yet existing methods are vulnerable to prevalent selection bias in SL data and reliant on cancer or tissue type-specific omics, which can be scarce. Notably, sequence similarity remains underexplored as a proxy for related gene function and joint essentiality.

Results

We propose ELISL, Early-Late Integrated SL prediction with forest ensembles, using context-free protein sequence embeddings and context-specific omics from cell lines and tissue. Across eight cancer types, ELISL showed superior robustne

SUBMITTER: Tepeli YI 

PROVIDER: S-EPMC11616771 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature