A probabilistic peptide machine for predicting hepatitis C virus protease cleavage sites.

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.