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

  • Alireza Shaneh
  • Gregory Butler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4013)


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.


Glycosylation Site Hide Unit Proteomic Data Bayesian Learning Window Frame 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Alireza Shaneh
    • 1
  • Gregory Butler
    • 1
  1. 1.Research Laboratory for Bioinformatics Technology, Department of Computer Science and Software EngineeringConcordia UniversityMontreal, QuebecCanada

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