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Heterodimeric protein complex identification by naïve Bayes classifiers.

Maruyama O - BMC Bioinformatics (2013)

Bottom Line: It can be found that in databases of protein complexes of yeast like CYC2008, the major type of known protein complexes is heterodimeric complexes.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Mathematics for Industry, Kyushu University, Fukuoka, Japan. om@imi.kyushu-u.ac.jp.

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 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/.

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Performance of trained classifiers by test sets. This graph shows the mean and standard deviation of precision, recall, and F-measure of the five trained classifiers. These scores are derived from five test sets of positive and negative examples.
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Figure 6: Performance of trained classifiers by test sets. This graph shows the mean and standard deviation of precision, recall, and F-measure of the five trained classifiers. These scores are derived from five test sets of positive and negative examples.

Mentions: For each test set, the performance measures of precision, recall, and F-measure are calculated. Their means with standard deviations are shown in Figure6. The precision, recall, and F-measure are averagely 0.818, 0.645, and 0.716, respectively. These scores are acceptable result. This feature set is also applied to several different sets of training and test, and quite similar results are obtained (data not shown).


Heterodimeric protein complex identification by naïve Bayes classifiers.

Maruyama O - BMC Bioinformatics (2013)

Performance of trained classifiers by test sets. This graph shows the mean and standard deviation of precision, recall, and F-measure of the five trained classifiers. These scores are derived from five test sets of positive and negative examples.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4219333&req=5

Figure 6: Performance of trained classifiers by test sets. This graph shows the mean and standard deviation of precision, recall, and F-measure of the five trained classifiers. These scores are derived from five test sets of positive and negative examples.
Mentions: For each test set, the performance measures of precision, recall, and F-measure are calculated. Their means with standard deviations are shown in Figure6. The precision, recall, and F-measure are averagely 0.818, 0.645, and 0.716, respectively. These scores are acceptable result. This feature set is also applied to several different sets of training and test, and quite similar results are obtained (data not shown).

Bottom Line: It can be found that in databases of protein complexes of yeast like CYC2008, the major type of known protein complexes is heterodimeric complexes.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Mathematics for Industry, Kyushu University, Fukuoka, Japan. om@imi.kyushu-u.ac.jp.

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 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/.

Show MeSH