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DISIS: Prediction of Drug Response through an Iterative Sure Independence Screening

Fang, Yun ; Qin, Yufang ; Zhang, Naiqian ; Wang, Jun ; Wang, Haiyun ; Zheng, Xiaoqi

PLoS One, Mar 2015, Vol.10(3), p.e0120408 [Peer Reviewed Journal]

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  • Title:
    DISIS: Prediction of Drug Response through an Iterative Sure Independence Screening
  • Author: Fang, Yun ; Qin, Yufang ; Zhang, Naiqian ; Wang, Jun ; Wang, Haiyun ; Zheng, Xiaoqi
  • Subjects: China ; Gene Expression ; Leukemia ; Brain Cancer ; Bioinformatics ; Iterative Methods ; Cancer ; Cancer Therapies ; Mutation ; Cancer ; Neural Networks ; Drugs ; Researchers ; Biomarkers ; Linear Programming ; Mathematics ; Genomes ; Tumors ; Kinases ; Alterations
  • Is Part Of: PLoS One, Mar 2015, Vol.10(3), p.e0120408
  • Description: 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.
  • Language: English
  • Identifier: E-ISSN: 19326203 ; DOI: 10.1371/journal.pone.0120408
  • Source: © ProQuest LLC All rights reserved
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