A sparse Bayesian position weighted bio-kernel network

Authors:
David C. Trudgian;Zheng Rong Yang
Affiliations:
School of Engineering, Computing, and Mathematics, University of Exeter, Exeter, UK;School of Engineering, Computing, and Mathematics, University of Exeter, Exeter, UK
Venue:
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Year:
2007

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Abstract

The Bio-Basis Function Neural Network (BBFNN) is a successful neural network architecture for peptide classification. However, the selection of a subset of peptides for a parsimonious network structure is always a difficult process. We present a Sparse Bayesian Bio-Kernel Network in which a minimal set of representative peptides can be selected automatically. We also introduce per-residue weighting to the Bio-Kernel to improve accuracy and identify patterns for biological activity. The new network is shown to outperform the original BBFNN on various datasets, covering different biological activities such as as enzymatic and post-translational-modification, and generates simple, interpretable models.