基于文本挖掘的抗癌药物反应预测特征选择。

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Bioinformatics advances Pub Date : 2024-03-26 eCollection Date: 2024-01-01 DOI:10.1093/bioadv/vbae047
Grace Wu, Arvin Zaker, Amirhosein Ebrahimi, Shivanshi Tripathi, Arvind Singh Mer
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引用次数: 0

摘要

动机:从基线基因组数据预测抗癌治疗反应是个性化医疗的一个关键障碍。机器学习方法通常用于从基因表达数据预测药物反应。在构建这些机器学习模型的过程中,最重要的挑战之一是从大量基因中识别出合适的特征:在这项研究中,我们利用了从科学文献文本挖掘中提取的特征(基因)。利用两个独立的癌症药物基因组数据集,我们证明了在机器学习任务中,基于文本挖掘的特征优于传统的特征选择技术。此外,我们的分析表明,在体外数据上训练的基于文本挖掘特征的机器学习模型在预测体内癌症模型的反应时也表现良好。我们的研究结果表明,基于文本挖掘的特征选择是一种易于实现的方法,适合用于建立抗癌药物反应预测的机器学习模型。可用性和实现:https://github.com/merlab/text_features。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Text-mining-based feature selection for anticancer drug response prediction.

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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