Integration of RNA Editing with Multiomics Data Improves Machine Learning Models for Predicting Drug Responses in Breast Cancer Patients.

ABSTRACT:

Background

The integration of conventional omics data such as genomics and transcriptomics data into artificial intelligence models has advanced significantly in recent years; however, their low applicability in clinical contexts, due to the high complexity of models, has been limited in their direct use inpatients. We integrated classic omics, including DNA mutation and RNA gene expression, added a novel focus on promising omics methods based on A>I(G) RNA editing, and developed a drug response prediction model.

Methods

We analyzed 104 patients from the Breast Cancer Genome-Guided Therapy Study (NCT02022202). This study was used to train (70%) with 10-fold cross-validation and test (30%) the drug response classification models. We assess the performance of the random forest

SUBMITTER: Bernal YA 

PROVIDER: S-EPMC11702790 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature