KinPred-RNA-kinase activity inference and cancer type classification using machine learning on RNA-seq data.

ABSTRACT: Kinases as important enzymes can transfer phosphate groups from high-energy and phosphate-donating molecules to specific substrates and play essential roles in various cellular processes. Existing algorithms for kinase activity from phosphorylated proteomics data are often costly, requiring valuable samples. Moreover, methods to extract kinase activities from bulk RNA sequencing data remain undeveloped. In this study, we propose a computational framework KinPred-RNA to derive kinase activities from bulk RNA-sequencing data in cancer samples. KinPred-RNA framework, using the extreme gradient boosting (XGBoost) regression model, outperforms random forest regression, multiple linear regression, and support vector machine regression models in predicting kinase activities from cancer-related RN

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC10959666 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature