Bayesian Learning for Feed-Forward Neural Network with Application to Proteomic Data: The Glycosylation Sites Detection of the Epidermal Growth Factor-Like Proteins Associated with Cancer as a Case Study

摘要

There are some neural network applications in proteomics; however, design and use of a neural network depends on the nature of the problem and the dataset studied. Bayesian framework is a consistent learning paradigm for a feed-forward neural network to infer knowledge from experimental data. Bayesian regularization automates the process of learning by pruning the unnecessary weights of a feed-forward neural network, a technique of which has been shown in this paper and applied to detect the glycosylation sites in epidermal growth factor-like repeat proteins involving in cancer as a case study. After applying the Bayesian framework, the number of network parameters decreased by 47.62%. The model performance comparing to One Step Secant method increased more than 34.92%. Bayesian learning produced more consistent outcomes than one step secant method did; however, it is computationally complex and slow, and the role of prior knowledge and its correlation with model selection should be further studied.