DISIS: prediction of drug response through an iterative sure independence screening.
DISIS: prediction of drug response through an iterative sure independence screening.
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DISIS：通过迭代的确定独立性筛选来预测药物反应。
DOI：
10.1371/journal.pone.0120408
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Zheng X
中科院分区：
文献类型：
--
作者：
Fang Y;Qin Y;Zhang N;Wang J;Wang H;Zheng X
基于基因组改变的药物反应预测是个性化医疗研究中的一项重要任务。目前的弹性网络模型利用一定的独立性筛选来选择与药物反应相关的基因组特征，但它可能忽略了一些稍弱特征的组合效应。在这项工作中，我们应用迭代确定独立性筛选方案从癌细胞系百科全书（CCLE）数据集中选择药物反应相关特征。对于CCLE中的每种药物，我们选择了多达40个特征，包括癌症相关基因的基因表达、突变和拷贝数改变，其中一些是显着强的特征，但与药物反应向量表现出弱边际相关性。基于所选特征的套索回归表明，对于大多数药物，我们的预测精度高于弹性网络回归的预测精度。
Prediction of drug response based on genomic alterations is an important task in the research of personalized medicine. Current elastic net model utilized a sure independence screening to select relevant genomic features with drug response, but it may neglect the combination effect of some marginally weak features. In this work, we applied an iterative sure independence screening scheme to select drug response relevant features from the Cancer Cell Line Encyclopedia (CCLE) dataset. For each drug in CCLE, we selected up to 40 features including gene expressions, mutation and copy number alterations of cancer-related genes, and some of them are significantly strong features but showing weak marginal correlation with drug response vector. Lasso regression based on the selected features showed that our prediction accuracies are higher than those by elastic net regression for most drugs.
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