Title :
Bayesian neural network for microarray data
Author :
Liang, Yulan ; George, E. Olusegum ; Kelemen, Arpad
Author_Institution :
Dept. of Math. Sci., Univ. of Memphis, TN, USA
fDate :
6/24/1905 12:00:00 AM
Abstract :
We propose Bayesian neural networks (BNN) with structural learning for exploring microarray data in gene expressions. The approach employs representative data and regularization to capture correlation among gene expressions and Bayesian techniques to extract gene expression information from noisy data. The performance was verified with stratified cross-validation and multiple iterated runs
Keywords :
Bayes methods; biology computing; data acquisition; learning (artificial intelligence); multilayer perceptrons; Bayesian neural network; DNA microarrays; data acquisition; gene expressions; microarray data; stratified cross-validation; structural learning; Bayesian methods; DNA; Data mining; Drugs; Fungi; Gene expression; Neural networks; Noise level; Noise measurement; Size measurement;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1005468
