  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.
