Table 6.

Driver prioritisation methods that utilise machine learning.

Software	Learning Model	Training Features	Primary Language	Year	Ref.
iCAGES	Support Vector Machine (SVM)	11 ANNOVAR mutation annotations	Perl	2016	Dong et al. [71]
sysSVM2	One-Class Support Vector Machine (SVM)	26 Features including: ANNOVAR mutation annotations, copy number, essentiality, tissue expression, genetic evolution and network interaction	R	2021	Nulsen et al . [72]
driveR	Lasso Regression Multi-Task Learning (MTL)	26 Features including: mutation annotation metaprediction, copy number, hotspot mutations, tissue-specific Phenolyzer score, KEGG cancer pathway membership	R	2021	Ulgen and Sezerman [73]
IMCDriver	Inductive Matrix Completion (IMC)	No external training features. Trained using similarity between samples (shared mutated genes) and similarity between genes (co-mutation across samples)	Python	2021	Zhang et al. [74]