Bayesian neural network for microarray data
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.
Proceedings of the International Joint Conference on Neural Networks
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