Title :
A Probabilistic Peptide Machine for Predicting Hepatitis C Virus Protease Cleavage Sites
Author :
Yang, Zheng Rong
Author_Institution :
Univ. of Exeter, Exeter
Abstract :
Although various machine learning approaches have been used for predicting protease cleavage sites, constructing a probabilistic model for these tasks is still challenging. This paper proposes a novel algorithm termed as a probabilistic peptide machine where estimating probability density functions and constructing a classifier for predicting protease cleavage sites are combined into one process. The simulation based on experimentally determined Hepatitis C virus (HCV) protease cleavage data has demonstrated the success of this new algorithm.
Keywords :
data mining; medical computing; microorganisms; probability; hepatitis C virus; probabilistic peptide machine; probability density function; protease cleavage sites; Amino acids; Data analysis; Inhibitors; Liver diseases; Machine learning; Machine learning algorithms; Peptides; Predictive models; Probability density function; Proteins; Cleavage site prediction; hepatitis C virus protease; probabilistic model; Algorithms; Artificial Intelligence; Binding Sites; Computer Simulation; Enzyme Activation; Models, Chemical; Peptides; Protein Binding; Sequence Analysis, Protein; Viral Nonstructural Proteins;
Journal_Title :
Information Technology in Biomedicine, IEEE Transactions on
DOI :
10.1109/TITB.2006.889314