Predicting hepatitis C virus protease cleavage sites using generalized linear indicator regression models.
This paper discusses how to predict hepatitis C virus protease cleavage sites in proteins using generalized linear indicator regression models. The mutual information is used for model-size optimization. Two simulation strategies are adopted, i.e., building a model based on published peptides and building a model based on the published peptides plus newly collected sequences. It is found that the latter outperforms the former significantly. The simulation also shows that the generalized linear indicator regression model far outperforms the multilayer perceptron model.
Yang
Zheng Rong
ZR
Department of Computer Science, University of Exeter, Exeter EX4 4QK, UK. z.r.yang@ex.ac.uk
D000465:Algorithms; D000595:Amino Acid Sequence; D001665:Binding Sites; D004789:Enzyme Activation; D016333:HIV Protease / Q000737:chemistry*; D016174:Hepacivirus / Q000201:enzymology*; D006868:Hydrolysis; D016014:Linear Models; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D011485:Protein Binding; D012044:Regression Analysis; D020539:Sequence Analysis, Protein / Q000379:methods*; D013379:Substrate Specificity
D016333:HIV Protease