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Exploiting ontology graph for predicting sparsely annotated gene function.

Wang S, Cho H, Zhai C, Berger B, Peng J - Bioinformatics (2015)

Bottom Line: Systematically predicting gene (or protein) function based on molecular interaction networks has become an important tool in refining and enhancing the existing annotation catalogs, such as the Gene Ontology (GO) database.Our method is scalable to datasets with a large number of annotations.In a cross-validation experiment in yeast, mouse and human, our method greatly outperformed previous state-of-the-art function prediction algorithms in predicting sparsely annotated functions, without sacrificing the performance on labels with sufficient information.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA and Department of Mathematics, MIT, Cambridge, MA, USA.

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A breakdown of GO labels by the number of annotated genes in (a) human and (b) yeast
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btv260-F1: A breakdown of GO labels by the number of annotated genes in (a) human and (b) yeast

Mentions: Despite the success of existing algorithms, a major difficulty that has not been sufficiently addressed is that of predicting rare labels. Because many molecular functions (MFs) are inherently specific in their scope, a large number of functional labels have only a few annotated genes (or positive annotations); for instance, in the human GO annotation database (Ashburner et al., 2000), there are currently 8626 GO labels with at least 3 annotations, 4178 of which have <10 annotated genes and 7905 labels have <100 genes. The distributions of GO labels with different numbers of annotations in yeast and human are shown in Figure 1. Nearly half of the GO labels have <10 annotations in both species.Fig. 1.


Exploiting ontology graph for predicting sparsely annotated gene function.

Wang S, Cho H, Zhai C, Berger B, Peng J - Bioinformatics (2015)

A breakdown of GO labels by the number of annotated genes in (a) human and (b) yeast
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4542782&req=5

btv260-F1: A breakdown of GO labels by the number of annotated genes in (a) human and (b) yeast
Mentions: Despite the success of existing algorithms, a major difficulty that has not been sufficiently addressed is that of predicting rare labels. Because many molecular functions (MFs) are inherently specific in their scope, a large number of functional labels have only a few annotated genes (or positive annotations); for instance, in the human GO annotation database (Ashburner et al., 2000), there are currently 8626 GO labels with at least 3 annotations, 4178 of which have <10 annotated genes and 7905 labels have <100 genes. The distributions of GO labels with different numbers of annotations in yeast and human are shown in Figure 1. Nearly half of the GO labels have <10 annotations in both species.Fig. 1.

Bottom Line: Systematically predicting gene (or protein) function based on molecular interaction networks has become an important tool in refining and enhancing the existing annotation catalogs, such as the Gene Ontology (GO) database.Our method is scalable to datasets with a large number of annotations.In a cross-validation experiment in yeast, mouse and human, our method greatly outperformed previous state-of-the-art function prediction algorithms in predicting sparsely annotated functions, without sacrificing the performance on labels with sufficient information.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA and Department of Mathematics, MIT, Cambridge, MA, USA.

Show MeSH
Related in: MedlinePlus