Bayesian neural network for microarray data

Authors

Yulan Liang, University of Memphis
E. Olusegun George, University of MemphisFollow
Arpad Kelemen, University of MemphisFollow

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.

Publication Title

Proceedings of the International Joint Conference on Neural Networks

Recommended Citation

Liang, Y., George, E., & Kelemen, A. (2002). Bayesian neural network for microarray data. Proceedings of the International Joint Conference on Neural Networks, 1, 193-197. Retrieved from https://digitalcommons.memphis.edu/facpubs/4278