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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.

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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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An example of a subgraph of a PPI network. This graph consists of an edge, e, and its adjacent edges with their weights.
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Figure 3: An example of a subgraph of a PPI network. This graph consists of an edge, e, and its adjacent edges with their weights.

Mentions: The first template is used to generate a score-type feature. The score-type feature with m, denoted by m.Score(e), is a function to just return the score of m for e, i.e.,m(e). Thus this is the most simplest feature. For example, the returned value is 30 for e in the graph in Figure3. This type features will work well if m itself is a good characterization of heterodimeric protein complexes.


Heterodimeric protein complex identification by naïve Bayes classifiers.

Maruyama O - BMC Bioinformatics (2013)

An example of a subgraph of a PPI network. This graph consists of an edge, e, and its adjacent edges with their weights.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: An example of a subgraph of a PPI network. This graph consists of an edge, e, and its adjacent edges with their weights.
Mentions: The first template is used to generate a score-type feature. The score-type feature with m, denoted by m.Score(e), is a function to just return the score of m for e, i.e.,m(e). Thus this is the most simplest feature. For example, the returned value is 30 for e in the graph in Figure3. This type features will work well if m itself is a good characterization of heterodimeric protein complexes.

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