title: Reduced bio-basis function neural networks for protease cleavage site prediction.
abstract: This paper presents a new neural learning algorithm for protease cleavage site prediction. The basic idea is to replace the radial basis function used in radial basis function neural networks by a so-called bio-basis function using amino acid similarity matrices. Mutual information is used to select bio-bases and a corresponding selection algorithm is developed. The algorithm has been applied to the prediction of HIV and Hepatitis C virus protease cleavage sites in proteins with success.
authors.affiliation: Department of Computer Science, Exeter University, Exeter EX4 4QF, UK. Z.R.Yang@exeter.ac.uk
authors.forename: Zheng Rong
authors.initials: ZR
authors.lastname: Yang
authors.forename: Emily A
authors.initials: EA
authors.lastname: Berry
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001665:Binding Sites; D003198:Computer Simulation; D004789:Enzyme Activation; D016333:HIV Protease / Q000737:chemistry; D008956:Models, Chemical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D010447:Peptide Hydrolases / Q000737:chemistry*; D011485:Protein Binding; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D013379:Substrate Specificity
chemical_list: D010447:Peptide Hydrolases; D016333:HIV Protease