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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Thomson, R., Hodgman, T.C., Yang, Z.R., Doyle, A.K.: Characterising proteolytic cleavage site activity using bio-basis function neural networks. Bioinformatics 19, 1741–1747 (2003)
Trudgian, D.C., Yang, Z.R.: Substitution Matrix Optimisation for Peptide Classification. Lecture Notes In Computer Science, vol. 4447, pp. 291–300. Springer, Heidelberg (2007)
Tipping, M.E.: Sparse Bayesian Learning and the Relevance Vector Machine. J. Machine Learning Res. 1, 211–244 (2001)
Qian, N., Sejnowski, T.J.: Predicting the secondary structure of globular proteins using neural network models. J. Mol. Biol. 202, 865–884 (1988)
MacKay, D.J.C.: Bayesian Interpolation. Neural Computation 4(3), 415–447 (1992)
Chou, K., Zhang, C., Kezdy, F.J., Poorman, R.A.: A Vector Projection Method for Predicting the Specificity of GalNAc-Transferase. Proteins 21, 118–126 (1995)
Cai, Y., Chou, K.: Artificial neural network model for predicting HIV protease cleavage sites in protein. Adv. in Eng. Software 29(2), 119–128 (1998)
Zhao, Y., Pinilla, C., Valmori, D., Martin, R., Simon, R.: Application of support vector machines for T-cell epitopes prediction. Bioinformatics 19(15), 1978–1984 (2003)
Hanley, J.A., McNeil, B.J.: The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1), 29–36 (1982)
Rognvaldsson, T., You, L.: Why neural networks should not be used for HIV-1 protease cleavage site prediction. Bioinformatics 20(11), 1702–1709 (2004)
You, L., Garwicz, D., Rognvaldsson, T.: Comprehensive Bioinformatic Analysis of the Specificity of Human Immunodeficiency Virus Type 1 Protease. J. Virology 79(19), 12477–12486 (2005)
Narayanan, A., Wu, X., Yang, Z.: Mining viral protease data to extract cleavage knowledge. Bioinformatics 18, S5–S13 (2002)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Trudgian, D.C., Yang, Z.R. (2007). A Sparse Bayesian Position Weighted Bio-Kernel Network. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2007. IDEAL 2007. Lecture Notes in Computer Science, vol 4881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77226-2_54
Download citation
DOI: https://doi.org/10.1007/978-3-540-77226-2_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77225-5
Online ISBN: 978-3-540-77226-2
eBook Packages: Computer ScienceComputer Science (R0)