A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data
作者: Tianyu KangWei DingLuoyan ZhangDaniel ZiemekKourosh Zarringhalam
作者单位: 1University of Massachusetts Boston
2Pfizer Worldwide Research & Development
刊名: BMC Bioinformatics, 2017, Vol.18 (1)
来源数据库: Springer Nature Journal
DOI: 10.1186/s12859-017-1984-2
关键词: Artificial neural networkGene regulatory networksPrediction of responseClinical trialGroup Lasso
英文摘要: Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we...
原始语种摘要: Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we...
全文获取路径: Springer Nature  (合作)
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影响因子:3.024 (2012)

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关键词翻译
关键词翻译
  • network 网络
  • neural 神经系统的
  • artificial 人为的
  • robust 牢固的
  • model 模型
  • regularization 正则化
  • biological 生物学的
  • shrinkage 收缩
  • simultaneous 同时的
  • machine 机器