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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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Micro ROC curve of predicting genes for new GO labels on MF in yeast
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btv260-F4: Micro ROC curve of predicting genes for new GO labels on MF in yeast

Mentions: As a proof-of-concept, we repeatedly held out one-third of the GO labels as the validation set of ‘uncharacterized’ labels. We then used the remaining two-third GO labels to learn the projection model and to predict genes that are associated with the held out labels. Figure 4 shows the result of this experiment in yeast. We observed that our framework achieves a promising performance on all categories with micro-AUROC ranging from 0.81 to 0.87. It is worth noting that, to our best knowledge, no other existing method is able to predict associated genes for new GO labels without any existing annotations. Disease gene prioritization is a closely related task where the goal is to predict genes associated with a particular disease, but most algorithms proposed for this problem also require an initial set of associated genes to be able to make predictions.Fig. 4.


Exploiting ontology graph for predicting sparsely annotated gene function.

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

Micro ROC curve of predicting genes for new GO labels on MF in yeast
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

btv260-F4: Micro ROC curve of predicting genes for new GO labels on MF in yeast
Mentions: As a proof-of-concept, we repeatedly held out one-third of the GO labels as the validation set of ‘uncharacterized’ labels. We then used the remaining two-third GO labels to learn the projection model and to predict genes that are associated with the held out labels. Figure 4 shows the result of this experiment in yeast. We observed that our framework achieves a promising performance on all categories with micro-AUROC ranging from 0.81 to 0.87. It is worth noting that, to our best knowledge, no other existing method is able to predict associated genes for new GO labels without any existing annotations. Disease gene prioritization is a closely related task where the goal is to predict genes associated with a particular disease, but most algorithms proposed for this problem also require an initial set of associated genes to be able to make predictions.Fig. 4.

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