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	Bayesian neural network approaches to ovarian cancer identification from high-resolution mass spectrometry data
	Yu, JS ; Chen, XW
	2005
关键词	GAUSSIAN-PROCESSES MONTE-CARLO CLASSIFICATION SERUM DIAGNOSTICS REGRESSION
英文摘要	Motivation: The classification of high-dimensional data is always a challenge to statistical machine learning. We propose a novel method named shallow feature selection that assigns each feature a probability of being selected based on the structure of training data itself. Independent of particular classifiers, the high dimension of biodata can be fleetly reduced to an applicable case for consequential processing. Moreover, to improve both efficiency and performance of classification, these prior probabilities are further used to specify the distributions of top-level hyperparameters in hierarchical models of Bayesian neural network (BNN), as well as the parameters in Gaussian process models. Results: Three BNN approaches were derived and then applied to identify ovarian cancer from NCI's high-resolution mass spectrometry data, which yielded an excellent performance in 1000 independent k-fold cross validations (k = 2,..., 10). For instance, indices of average sensitivity and specificity of 98.56 and 98.42%, respectively, were achieved in the 2-fold cross validations. Furthermore, only one control and one cancer were misclassified in the leave-one-out cross validation. Some other popular classifiers were also tested for comparison.; http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000230273000055&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701 ; Biochemical Research Methods; Biotechnology & Applied Microbiology; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Statistics & Probability; SCI(E); CPCI-S(ISTP); PubMed; 20
语种	英语
DOI标识	10.1093/bioinformatics/bti1030
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/346347]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Yu, JS,Chen, XW. Bayesian neural network approaches to ovarian cancer identification from high-resolution mass spectrometry data. 2005-01-01.

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