Text-mining-based feature selection for anticancer drug response prediction

In this study, we utilize features (genes) extracted using the text-mining of scientific literatures. Using two independent cancer pharmacogenomic datasets, we demonstrate that text-mining-based features outperform traditional feature selection techniques in machine learning tasks. In addition, our analysis reveals that text-mining feature-based machine learning models trained on in vitro data also perform well when predicting the response of in vivo cancer models. Our results demonstrate that text-mining-based feature selection is an easy to implement approach that is suitable for building machine learning models for anticancer drug response prediction.AVAILABILITY AND IMPLEMENTATION: https://github.com/merlab/text_features.PMID:38606185 | PMC:PMC11009020 | DOI:10.1093/bioadv/vbae047

Source: Adv Data - April 12, 2024 Category: Epidemiology Authors: Grace Wu Arvin Zaker Amirhosein Ebrahimi Shivanshi Tripathi Arvind Singh Mer Source Type: research