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Towards a semi-automatic functional annotation tool based on decision-tree techniques.

Azé J, Gentils L, Toffano-Nioche C, Loux V, Gibrat JF, Bessières P, Rouveirol C, Poupon A, Froidevaux C - BMC Proc (2008)

Bottom Line: We tested the two methods, first independently, then in a combined approach, and evaluated the obtained results using hierarchical evaluation measures.The combination of the two approaches is very encouraging and we will further refine these combinations in order to get rules even more useful for the annotators.This first study is a crucial step towards designing a semi-automatic functional annotation tool.

View Article: PubMed Central - HTML - PubMed

Affiliation: LRI - CNRS UMR 8623 - University Paris-Sud 11, F-91405 Orsay Cedex, France. Jerome.Aze@lri.fr

ABSTRACT

Background: Due to the continuous improvements of high throughput technologies and experimental procedures, the number of sequenced genomes is increasing exponentially. Ultimately, the task of annotating these data relies on the expertise of biologists. The necessity for annotation to be supervised by human experts is the rate limiting step of the data analysis. To face the deluge of new genomic data, the need for automating, as much as possible, the annotation process becomes critical.

Results: We consider annotation of a protein with terms of the functional hierarchy that has been used to annotate Bacillus subtilis and propose a set of rules that predict classes in terms of elements of the functional hierarchy, i.e., a class is a node or a leaf of the hierarchy tree. The rules are obtained through two decision-trees techniques: first-order decision-trees and multilabel attribute-value decision-trees, by using as training data the proteins from two lactic bacteria: Lactobacillus sakei and Lactobacillus bulgaricus. We tested the two methods, first independently, then in a combined approach, and evaluated the obtained results using hierarchical evaluation measures. Results obtained for the two approaches on both genomes are comparable and show a good precision together with a high prediction rate. Using combined approaches increases the recall and the prediction rate.

Conclusion: The combination of the two approaches is very encouraging and we will further refine these combinations in order to get rules even more useful for the annotators. This first study is a crucial step towards designing a semi-automatic functional annotation tool.

No MeSH data available.


Example of rules. Example of rules obtained with TILDE and Multilabel
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Figure 6: Example of rules. Example of rules obtained with TILDE and Multilabel

Mentions: Fig. 6 presents an example of the rules obtained with TILDE and Multilabel for protein esa800 of L. sakei. The trees were learnt with the proteins of L. bulgaricus. Fig. 7 shows the trees produced with TILDE at each level (for the first two levels only the fragment of the tree of interest is displayed). The rules correspond to paths in these trees. The meaning of the GO-terms is given in Tab. 4 together with a mapping that most biologists would do of these terms on the functional hierarchy.


Towards a semi-automatic functional annotation tool based on decision-tree techniques.

Azé J, Gentils L, Toffano-Nioche C, Loux V, Gibrat JF, Bessières P, Rouveirol C, Poupon A, Froidevaux C - BMC Proc (2008)

Example of rules. Example of rules obtained with TILDE and Multilabel
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2654970&req=5

Figure 6: Example of rules. Example of rules obtained with TILDE and Multilabel
Mentions: Fig. 6 presents an example of the rules obtained with TILDE and Multilabel for protein esa800 of L. sakei. The trees were learnt with the proteins of L. bulgaricus. Fig. 7 shows the trees produced with TILDE at each level (for the first two levels only the fragment of the tree of interest is displayed). The rules correspond to paths in these trees. The meaning of the GO-terms is given in Tab. 4 together with a mapping that most biologists would do of these terms on the functional hierarchy.

Bottom Line: We tested the two methods, first independently, then in a combined approach, and evaluated the obtained results using hierarchical evaluation measures.The combination of the two approaches is very encouraging and we will further refine these combinations in order to get rules even more useful for the annotators.This first study is a crucial step towards designing a semi-automatic functional annotation tool.

View Article: PubMed Central - HTML - PubMed

Affiliation: LRI - CNRS UMR 8623 - University Paris-Sud 11, F-91405 Orsay Cedex, France. Jerome.Aze@lri.fr

ABSTRACT

Background: Due to the continuous improvements of high throughput technologies and experimental procedures, the number of sequenced genomes is increasing exponentially. Ultimately, the task of annotating these data relies on the expertise of biologists. The necessity for annotation to be supervised by human experts is the rate limiting step of the data analysis. To face the deluge of new genomic data, the need for automating, as much as possible, the annotation process becomes critical.

Results: We consider annotation of a protein with terms of the functional hierarchy that has been used to annotate Bacillus subtilis and propose a set of rules that predict classes in terms of elements of the functional hierarchy, i.e., a class is a node or a leaf of the hierarchy tree. The rules are obtained through two decision-trees techniques: first-order decision-trees and multilabel attribute-value decision-trees, by using as training data the proteins from two lactic bacteria: Lactobacillus sakei and Lactobacillus bulgaricus. We tested the two methods, first independently, then in a combined approach, and evaluated the obtained results using hierarchical evaluation measures. Results obtained for the two approaches on both genomes are comparable and show a good precision together with a high prediction rate. Using combined approaches increases the recall and the prediction rate.

Conclusion: The combination of the two approaches is very encouraging and we will further refine these combinations in order to get rules even more useful for the annotators. This first study is a crucial step towards designing a semi-automatic functional annotation tool.

No MeSH data available.