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一种基于集成的用于NCI-DREAM药物敏感性预测挑战赛的顶级方法。

An ensemble based top performing approach for NCI-DREAM drug sensitivity prediction challenge.

作者信息

Wan Qian, Pal Ranadip

机构信息

Electrical and Computer Engineering, Texas Tech University, Lubbock, Texas, United States of America.

出版信息

PLoS One. 2014 Jun 30;9(6):e101183. doi: 10.1371/journal.pone.0101183. eCollection 2014.

Abstract

We consider the problem of predicting sensitivity of cancer cell lines to new drugs based on supervised learning on genomic profiles. The genetic and epigenetic characterization of a cell line provides observations on various aspects of regulation including DNA copy number variations, gene expression, DNA methylation and protein abundance. To extract relevant information from the various data types, we applied a random forest based approach to generate sensitivity predictions from each type of data and combined the predictions in a linear regression model to generate the final drug sensitivity prediction. Our approach when applied to the NCI-DREAM drug sensitivity prediction challenge was a top performer among 47 teams and produced high accuracy predictions. Our results show that the incorporation of multiple genomic characterizations lowered the mean and variance of the estimated bootstrap prediction error. We also applied our approach to the Cancer Cell Line Encyclopedia database for sensitivity prediction and the ability to extract the top targets of an anti-cancer drug. The results illustrate the effectiveness of our approach in predicting drug sensitivity from heterogeneous genomic datasets.

摘要

我们考虑基于基因组图谱的监督学习来预测癌细胞系对新药敏感性的问题。细胞系的遗传和表观遗传特征提供了关于调控各个方面的观察结果,包括DNA拷贝数变异、基因表达、DNA甲基化和蛋白质丰度。为了从各种数据类型中提取相关信息,我们应用了一种基于随机森林的方法,从每种数据类型生成敏感性预测,并将这些预测组合到一个线性回归模型中,以生成最终的药物敏感性预测。我们的方法应用于NCI-DREAM药物敏感性预测挑战赛时,在47个团队中表现出色,产生了高精度的预测。我们的结果表明,纳入多种基因组特征降低了估计的自助抽样预测误差的均值和方差。我们还将我们的方法应用于癌症细胞系百科全书数据库进行敏感性预测以及提取抗癌药物的顶级靶点。结果说明了我们的方法在从异质基因组数据集中预测药物敏感性方面的有效性。

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