Precision oncology research also benefits from a variety of alternative ML approaches for supervised and unsupervised pattern analysis in datasets originating from multiple sources, including tumor-derived omic profiles. This includes, for example, the prediction of oncogenes and tumor suppressors with random forests. Key examples of (non-DL) ML techniques include: probabilistic models, kernel-based models (e.g., support vector machines), and decision tree-based models (e.g., random forests and gradient boosting machines; GBM). These and other approaches have provided the basis for promising predictive modelling applications in oncology research.
To date, ML has played a prominent role in facilitating novel applications that mainly rely on the supervised identification, correlation, and classification of complex data patterns for patient stratification. However, to deliver on the promise of a more precise prevention, detection, and treatment of cancers, other clinically-oriented computational modelling challenges must be tackled. This perspective underlines a selection of such research challenges or requirements for moving the field forward. It argues that ML offers, yet to be fully tapped, opportunities for enabling precision oncology far beyond relatively well-known applications, such as the supervised classification of single-source omics or imaging datasets. Moreover, there is a need for modelling approaches that can assist researchers and clinicians in better understanding biological causality. To advance and accelerate precision oncology research, the scope of questions and applications that AI can address ought to be considerably expanded
