Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review.

ABSTRACT: The identification of biomarkers plays a crucial role in personalized medicine, both in the clinical and research settings. However, the contrast between predictive and prognostic biomarkers can be challenging due to the overlap between the two. A prognostic biomarker predicts the future outcome of cancer, regardless of treatment, and a predictive biomarker predicts the effectiveness of a therapeutic intervention. Misclassifying a prognostic biomarker as predictive (or vice versa) can have serious financial and personal consequences for patients. To address this issue, various statistical and machine learning approaches have been developed. The aim of this study is to present an in-depth analysis of recent advancements, trends, challenges, and future prospects in biomarker identification.

SUBMITTER: Al-Tashi Q 

PROVIDER: S-EPMC10178491 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature