Current cancer driver variant predictors learn to recognize driver genes instead of functional variants.

Journal: BMC biology

PMID: 33441128

Abstract

BACKGROUND: Identifying variants that drive tumor progression (driver variants) and distinguishing these from variants that are a byproduct of the uncontrolled cell growth in cancer (passenger variants) is a crucial step for understanding tumorigenesis and precision oncology. Various bioinformatics methods have attempted to solve this complex task.

Authors

Daniele Raimondi

Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, La Plaine Campus, Triomflaan.
Antoine Passemiers

ESAT-STADIUS, KU Leuven, Leuven, 3001, Belgium.
Piero Fariselli
Yves Moreau

ESAT-STADIUS, KU Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium.

Keywords

Carcinogenesis Disease Progression Machine Learning Medical Oncology Neoplasms Precision Medicine

External Resources

View on PubMed Access via DOI PubMed (33441128)

Current cancer driver variant predictors learn to recognize driver genes instead of functional variants.

Abstract

Authors

Keywords

External Resources

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