Abstract
The Bio-basis Function Neural Network (BBFNN) is a novel neural architecture for peptide classification that makes use of amino acid mutation matrices and a similarity function to model protein peptide data without encoding. This study presents an Evolutionary Bio-basis network (EBBN), an extension to the BBFNN that uses a self adapting Evolution Strategy to optimise a problem specific substitution matrix for much improved model performance. The EBBN is assessed against BBFNN and multi layer perceptron (MLP) models using three datasets covering cleavage sites, epitope sites, and glycoprotein linkage sites. The method exhibits statistically significant improvements in performance for two of these sets.
Keywords
- Functional Site
- Multi Layer Perceptron
- Substitution Matrix
- Mutation Strength
- Positive Peptide
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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Trudgian, D.C., Yang, Z.R. (2007). Substitution Matrix Optimisation for Peptide Classification. In: Marchiori, E., Moore, J.H., Rajapakse, J.C. (eds) Evolutionary Computation,Machine Learning and Data Mining in Bioinformatics. EvoBIO 2007. Lecture Notes in Computer Science, vol 4447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71783-6_28
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DOI: https://doi.org/10.1007/978-3-540-71783-6_28
Publisher Name: Springer, Berlin, Heidelberg
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