MarkerPredict: predicting clinically relevant predictive biomarkers with machine learning.

ABSTRACT: Precision oncology relies on predictive biomarkers for selecting targeted cancer therapies. Network-based properties of proteins, together with structural features such as intrinsic disorder, are likely to shape their potential as biomarkers. We therefore designed a hypothesis-generating framework that integrates network motifs and protein disorder to explore their contribution to predictive biomarker discovery. This encouraged us to develop MarkerPredict by using literature evidence-based positive and negative training sets of 880 target-interacting protein pairs total with Random Forest and XGBoost machine learning models on three signalling networks. MarkerPredict classified 3670 target-neighbour pairs with 32 different models achieving a 0.7-0.96 LOOCV accuracy. We defined a Biomarker

SUBMITTER: Veres DV 

PROVIDER: S-EPMC12638940 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature