A bio-basis function neural network for protein peptide cleavage activity characterisation.
This paper presents a novel neural learning algorithm for analysing protein peptides which comprise amino acids as non-numerical attributes. The algorithm is derived from the radial basis function neural networks (RBFNNs) and is referred to as a bio-basis function neural network (BBFNN). The basic principle is to replace the radial basis function used by RBFNNs with a bio-basis function. Each basis in BBFNN is supported by a peptide. The bases collectively form a feature space, in which each basis represents a feature dimension. A linear classifier is constructed in the feature space for characterising a protein peptide in terms of functional status. The theoretical basis of BBFNN is that peptides, which perform the same function will have similar compositions of amino acids. Because of this, the similarity between peptides can have statistical significance for modelling while the proposed bio-basis function can well code this information from data. The application to two real cases shows that BBFNN outperformed multi-layer perceptrons and support vector machines.
Department of Computer Science, University of Exeter, Northcote House, The Queen's Drive, Exeter EX4 4QJ, UK. z.r.yang@ex.ac.uk
Zheng Rong
ZR
Yang
Jonathan
J
Dry
Rebecca
R
Thomson
T
T
Charles Hodgman
D000465:Algorithms; D000818:Animals; D005170:Factor X / Q000378:metabolism; D006801:Humans; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D010455:Peptides / Q000378:metabolism; D011506:Proteins / Q000378:metabolism*; D012372:ROC Curve; D014357:Trypsin / Q000378:metabolism
D010455:Peptides; D011506:Proteins; D005170:Factor X; D014357:Trypsin