A multivariate random forest based framework for drug sensitivity prediction

Qian Wan, R. Pal
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引用次数: 3

Abstract

Drug sensitivity prediction based on genomic characterization remains a significant challenge in the area of systems medicine. Multiple approaches have been proposed for mapping genomic characterization to drug sensitivity and among them ensemble based learning techniques like Random Forests (RF) have been a top performer [1, 2]. The majority of the current approaches infer a predictive model for each drug individually but correlation between different drug sensitivities suggests that multiple response prediction incorporating the co-variance of the different drug responses can possibly improve prediction accuracy. In this abstract, we report a prediction and analysis framework based on Multivariate Random Forests (MRF) that incorporates the correlation between different drug sensitivities.
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基于多变量随机森林的药物敏感性预测框架
基于基因组特征的药物敏感性预测仍然是系统医学领域的一个重大挑战。已经提出了多种方法来将基因组特征映射到药物敏感性,其中基于集成的学习技术,如随机森林(Random Forests, RF)已成为表现最好的方法[1,2]。目前大多数方法都是针对每种药物单独推断预测模型,但不同药物敏感性之间的相关性表明,结合不同药物反应协方差的多重反应预测可能会提高预测精度。在这篇摘要中,我们报告了一个基于多元随机森林(MRF)的预测和分析框架,该框架结合了不同药物敏感性之间的相关性。
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