Heterodimeric protein complex identification by naive Bayes classifiers.

ABSTRACT:

Background

Protein complexes are basic cellular entities that carry out the functions of their components. It can be found that in databases of protein complexes of yeast like CYC2008, the major type of known protein complexes is heterodimeric complexes. Although a number of methods for trying to predict sets of proteins that form arbitrary types of protein complexes simultaneously have been proposed, it can be found that they often fail to predict heterodimeric complexes.

Results

In this paper, we have designed several features characterizing heterodimeric protein complexes based on genomic data sets, and proposed a supervised-learning method for the prediction of heterodimeric protein complexes. This method learns the parameters of the features, which are embedded in the n

SUBMITTER: Maruyama O 

PROVIDER: S-EPMC4219333 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature