CDMPred: a tool for predicting cancer driver missense mutations with high-quality passenger mutations.

ABSTRACT: Most computational methods for predicting driver mutations have been trained using positive samples, while negative samples are typically derived from statistical methods or putative samples. The representativeness of these negative samples in capturing the diversity of passenger mutations remains to be determined. To tackle these issues, we curated a balanced dataset comprising driver mutations sourced from the COSMIC database and high-quality passenger mutations obtained from the Cancer Passenger Mutation database. Subsequently, we encoded the distinctive features of these mutations. Utilizing feature correlation analysis, we developed a cancer driver missense mutation predictor called CDMPred employing feature selection through the ensemble learning technique XGBoost. The proposed CDMPr

SUBMITTER: Wang L 

PROVIDER: S-EPMC11382650 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature