Machine learning identifies a core gene set predictive of acquired resistance to EGFR tyrosine kinase inhibitor.

ABSTRACT:

Purpose

Acquired resistance (AR) to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) is a major issue worldwide, for both patients and healthcare providers. However, precise prediction is currently infeasible due to the lack of an appropriate model. This study was conducted to develop and validate an individualized prediction model for automated detection of acquired EGFR-TKI resistance.

Methods

Penalized regression was applied to construct a predictive model using publically available genomic cohorts of acquired EGFR-TKI resistance. To develop a model with enhanced generalizability, we merged multiple cohorts then updated the learning parameter via robust cross-study validation. Model performance was evaluated mainly using the area under the receiver

SUBMITTER: Kim YR 

PROVIDER: S-EPMC11813524 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature