Text-mining-based feature selection for anticancer drug response prediction
Abstract Motivation Predicting anticancer treatment response from baseline genomic data is a critical obstacle in personalized medicine. Machine learning methods are commonly used for predicting drug response from gene expression data. In the process of constructing these machine learning models, on...
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Published in | Bioinformatics advances Vol. 4; no. 1; p. vbae047 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
England
Oxford University Press
2024
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Subjects | |
Online Access | Get full text |
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Summary: | Abstract
Motivation
Predicting anticancer treatment response from baseline genomic data is a critical obstacle in personalized medicine. Machine learning methods are commonly used for predicting drug response from gene expression data. In the process of constructing these machine learning models, one of the most significant challenges is identifying appropriate features among a massive number of genes.
Results
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. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Grace Wu and Arvin Zaker Equal contribution. |
ISSN: | 2635-0041 2635-0041 |
DOI: | 10.1093/bioadv/vbae047 |