tailieunhanh - Báo cáo hóa học: " Gene Prediction Using Multinomial Probit Regression with Bayesian Gene Selection"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Gene Prediction Using Multinomial Probit Regression with Bayesian Gene Selection | EURASIP Journal on Applied Signal Processing 2004 1 115-124 2004 Hindawi Publishing Corporation Gene Prediction Using Multinomial Probit Regression with Bayesian Gene Selection Xiaobo Zhou Department of Electrical Engineering Texas A M University College Station TX 77843 USA Email zxb@ Xiaodong Wang Department of Electrical Engineering Columbia University New York NY 10027 USA Email wangx@ Edward R. Dougherty Department of Electrical Engineering Texas A M University 3128 TAMU College Station TX 77843-3128 USA Department of Pathology University of Texas MD Anderson Cancer Center Houstan TX 77030 USA Email e-dougherty@ Received 3 April 2003 Revised 1 September 2003 A critical issue for the construction of genetic regulatory networks is the identification of network topology from data. In the context of deterministic and probabilistic Boolean networks as well as their extension to multilevel quantization this issue is related to the more general problem of expression prediction in which we want to find small subsets of genes to be used as predictors of target genes. Given some maximum number of predictors to be used a full search of all possible predictor sets is combinatorially prohibitive except for small predictors sets and even then may require supercomputing. Hence suboptimal approaches to finding predictor sets and network topologies are desirable. This paper considers Bayesian variable selection for prediction using a multinomial probit regression model with data augmentation to turn the multinomial problem into a sequence of smoothing problems. There are multiple regression equations and we want to select the same strongest genes for all regression equations to constitute a target predictor set or in the context of a genetic network the dependency set for the target. The probit regressor is approximated as a linear combination of the genes and a Gibbs sampler is employed to find the strongest genes. Numerical techniques to .