Precision oncology: a review to assess interpretability in several explainable methods.

ABSTRACT: Great efforts have been made to develop precision medicine-based treatments using machine learning. In this field, where the goal is to provide the optimal treatment for each patient based on his/her medical history and genomic characteristics, it is not sufficient to make excellent predictions. The challenge is to understand and trust the model's decisions while also being able to easily implement it. However, one of the issues with machine learning algorithms-particularly deep learning-is their lack of interpretability. This review compares six different machine learning methods to provide guidance for defining interpretability by focusing on accuracy, multi-omics capability, explainability and implementability. Our selection of algorithms includes tree-, regression- and kernel-based met

SUBMITTER: Gimeno M 

PROVIDER: S-EPMC10359088 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature