Prediction of Acquired Taxane Resistance Using a Personalized Pathway-Based Machine Learning Method.

ABSTRACT:

Purpose

This study was conducted to develop and validate an individualized prediction model for automated detection of acquired taxane resistance (ATR).

Materials and methods

Penalized regression, combinedwith an individualized pathway score algorithm,was applied to construct a predictive model using publically available genomic cohorts of ATR and intrinsic taxane resistance (ITR). To develop a model with enhanced generalizability, we merged multiple ATR studies then updated the learning parameter via robust cross-study validation.

Results

For internal cross-study validation, the ATR model produced a perfect performance with an overall area under the receiver operating curve (AUROC) of 1.000 with an area under the precision-recall curve (AUPRC) of 1.000, a Brier score

SUBMITTER: Kim YR 

PROVIDER: S-EPMC6473276 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature