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

Journal: Cancer research and treatment

Published Date: Apr 1, 2019

Abstract

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

Authors

Young Rae Kim

Department of Biochemistry, Konkuk University School of Medicine, Seoul, 143-701, Republic of Korea.
Dongha Kim

Department of Biochemistry, Konkuk University School of Medicine, Seoul, Korea.
Sung Young Kim

Department of Biochemistry, Konkuk University School of Medicine, Seoul, 143-701, Republic of Korea. palelamp@kku.ac.kr.

Keywords

Algorithms Antineoplastic Agents Bridged-Ring Compounds Databases, Genetic Drug Resistance, Neoplasm Gene Expression Profiling Machine Learning Models, Statistical Pharmacogenetics Precision Medicine Reproducibility of Results ROC Curve Taxoids

External Resources

View on PubMed Access via DOI PubMed (30092623)

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

Abstract

Authors

Keywords

External Resources

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