Machine learning-based identification of proteomic markers in colorectal cancer using UK Biobank data.

ABSTRACT: Colorectal cancer is one of the leading causes of cancer-related mortality in the world. Incidence and mortality are predicted to rise globally during the next several decades. When detected early, colorectal cancer is treatable with surgery and medications. This leads to the requirement for prognostic and diagnostic biomarker development. Our study integrates machine learning models and protein network analysis to identify protein biomarkers for colorectal cancer. Our methodology leverages an extensive collection of proteome profiles from both healthy and colorectal cancer individuals. To identify a potential biomarker with high predictive ability, we used three machine learning models. To enhance the interpretability of our models, we quantify each protein's contribution to the model's p

SUBMITTER: Radhakrishnan SK 

PROVIDER: S-EPMC11746037 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature