Application of transfer learning for cancer drug sensitivity prediction

被引:0
作者
Saugato Rahman Dhruba
Raziur Rahman
Kevin Matlock
Souparno Ghosh
Ranadip Pal
机构
[1] Department of Electrical and Computer Engineering,
[2] Texas Tech University,undefined
[3] Department of Mathematics and Statistics,undefined
[4] Texas Tech University,undefined
来源
BMC Bioinformatics | / 19卷
关键词
Drug sensitivity prediction; Pharmacogenomic studies; CCLE; GDSC; Transfer learning; Nonlinear mapping; Latent variable; Cost optimization;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
相关论文
共 50 条
[1]  
Yang W(2013)Genomics of drug sensitivity in cancer (gdsc): a resource for therapeutic biomarker discovery in cancer cells Nucleic Acids Res 41 955-61
[2]  
Soares J(2012)The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity Nature 483 603-7
[3]  
Greninger P(2010)A survey on transfer learning IEEE Trans Knowl Data Eng 22 1345-59
[4]  
Edelman EJ(2016)A survey of transfer learning J Big Data 3 9-93
[5]  
Lightfoot H(2013)Inconsistency in large pharmacogenomic studies Nature 504 389-32
[6]  
Forbes S(2016)Extracting a low-dimensional description of multiple gene expression datasets reveals a potential driver for tumor-associated stroma in ovarian cancer Genome Med 8 66-1410
[7]  
Bindal N(2001)Random forests Mach Learn 45 5-6
[8]  
Beare D(2017)Integratedmrf: random forest-based framework for integrating prediction from different data types Bioinformatics (Oxford, England) 33 1407-60
[9]  
Smith JA(2015)Bias corrections for random forest in regression using residual rotation J Korean Stat Soc 44 321-undefined
[10]  
Thompson IR(2015)Design of probabilistic random forests with applications to anticancer drug sensitivity prediction Cancer Informat 14 57-undefined