Bayesian neural networks for detecting epistasis in genetic association studies

被引:0
作者
Andrew L Beam
Alison Motsinger-Reif
Jon Doyle
机构
[1] Harvard Medical School,Center for Biomedical Informatics
[2] North Carolina State University,Bioinformatics Research Center
[3] North Carolina State University,Department of Statistics
[4] North Carolina State University,Department of Computer Science
来源
BMC Bioinformatics | / 15卷
关键词
Markov Chain Monte Carlo; Hide Unit; Multifactor Dimensionality Reduction; Causal SNPs; Boost Decision Tree;
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 162 条
[11]  
Cho JH(2004)Screening large-scale association study data: exploiting interactions using random forests BMC genetics 5 32-1232
[12]  
Guttmacher AE(2011)Detecting epistatic effects in association studies at a genomic level based on an ensemble approach Bioinformatics 27 222-378
[13]  
Kong A(2011)Learning genetic epistasis using Bayesian network scoring criteria BMC Bioinformatics 12 89-2105-12-89-25
[14]  
Kruglyak L(2006)Gene selection and classification of microarray data using random forest BMC Bioinformatics 7 3-211
[15]  
Mardis E(2001)Random forests Mach Learning 45 5-1092
[16]  
Rotimi CN(2001)Greedy function approximation: a gradient boosting machine. (English summary) Ann. Statist 29 1189-109
[17]  
Slatkin M(2002)Stochastic gradient boosting Comput Stat Data Anal 38 367-349
[18]  
Valle D(2003)A Bayesian neural network approach for modelling censored data with an application to prognosis after surgery for breast cancer Artif Intell Med 28 1-14
[19]  
Whittemore AS(2002)Bayesian neural network learning for repeat purchase modelling in direct marketing Eur J Oper Res 138 191-366
[20]  
Boehnke M(2004)Equation of state calculations by fast computing machines J Chem Phys 21 1087-143