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 naïve Bayes classifier. The log-likelihood ratio derived from the naïve 
Bayes classifier with the parameter values obtained by maximum likelihood 
estimation gives the score of a given pair of proteins to predict whether the 
pair is a heterodimeric complex or not. A five-fold cross-validation shows good 
performance on yeast. The trained classifiers also show higher predictability 
than various existing algorithms on yeast data sets with approximate and exact 
matching criteria.
CONCLUSIONS: Heterodimeric protein complex prediction is a rather harder problem 
than heteromeric protein complex prediction because heterodimeric protein 
complex is topologically simpler. However, it turns out that by designing 
features specialized for heterodimeric protein complexes, predictability of them 
can be improved. Thus, the design of more sophisticate features for 
heterodimeric protein complexes as well as the accumulation of more accurate and 
useful genome-wide data sets will lead to higher predictability of heterodimeric 
protein complexes. Our tool can be downloaded from 
http://imi.kyushu-u.ac.jp/~om/.