Machine learning methods for prediction of cancer driver genes: a survey paper

TL;DR

This survey reviews machine learning approaches for identifying cancer driver genes, highlighting recent advances, challenges, and future directions in computational cancer genomics.

Contribution

It provides a comprehensive overview of ML-based methods for cancer driver gene prediction, emphasizing data types, algorithms, and current limitations.

Findings

ML methods have significantly advanced cancer driver gene identification.

Interactions among data types and algorithms are crucial for model performance.

Current analytical limitations point to areas for future research.

Abstract

Identifying the genes and mutations that drive the emergence of tumors is a major step to improve understanding of cancer and identify new directions for disease diagnosis and treatment. Despite the large volume of genomics data, the precise detection of driver mutations and their carrying genes, known as cancer driver genes, from the millions of possible somatic mutations remains a challenge. Computational methods play an increasingly important role in identifying genomic patterns associated with cancer drivers and developing models to predict driver events. Machine learning (ML) has been the engine behind many of these efforts and provides excellent opportunities for tackling remaining gaps in the field. Thus, this survey aims to perform a comprehensive analysis of ML-based computational approaches to identify cancer driver mutations and genes, providing an integrated, panoramic view of the broad data and algorithmic landscape within this scientific problem. We discuss how the interactions among data types and ML algorithms have been explored in previous solutions and outline current analytical limitations that deserve further attention from the scientific community. We hope that by helping readers become more familiar with significant developments in the field brought by ML, we may inspire new researchers to address open problems and advance our knowledge towards cancer driver discovery.