close
Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2013;14 Suppl 3(Suppl 3):S10.
doi: 10.1186/1471-2105-14-S3-S10. Epub 2013 Feb 28.

Combining heterogeneous data sources for accurate functional annotation of proteins

Artem Sokolov  1 , Christopher Funk, Kiley Graim, Karin Verspoor, Asa Ben-Hur
Affiliations

Combining heterogeneous data sources for accurate functional annotation of proteins

Artem Sokolov et al. BMC Bioinformatics. 2013.

Abstract

Combining heterogeneous sources of data is essential for accurate prediction of protein function. The task is complicated by the fact that while sequence-based features can be readily compared across species, most other data are species-specific. In this paper, we present a multi-view extension to GOstruct, a structured-output framework for function annotation of proteins. The extended framework can learn from disparate data sources, with each data source provided to the framework in the form of a kernel. Our empirical results demonstrate that the multi-view framework is able to utilize all available information, yielding better performance than sequence-based models trained across species and models trained from collections of data within a given species. This version of GOstruct participated in the recent Critical Assessment of Functional Annotations (CAFA) challenge; since then we have significantly improved the natural language processing component of the method, which now provides performance that is on par with that provided by sequence information. The GOstruct framework is available for download at http://strut.sourceforge.net.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Graphical representation of the training objective for structured-output methods. Training examples are displayed along the horizontal axis. The structured SVM aims to maximize the margin between the compatibility values for the true label and all other labels, as depicted with the two dashed lines. Example x1 satisfies this. Example x2, while correctly classified, has a margin violation. Example x3 is misclassified. For demonstration purposes, we assume that the highest compatibility values for the three presented examples are all equal to each other.
Figure 2
Figure 2
The multi-view approach. Data is separated into two views: a cross-species view that contains features computed from sequence, and a species-specific view that contains features computed from PPIs, gene expression and protein-GO term co-mention in mouse. A separate classifier is trained on the data from each view; the multi-view classifier uses the sum of the two compatibility functions.
Figure 3
Figure 3
The chain classifier approach. Predictions from the cross-species view are provided as features to the species-specific view, along with other data.
Figure 4
Figure 4
The distribution of the GO term depth in the annotations provided by the dataset. Term depth is computed as the length of the longest path to the root of the corresponding ontology.
Figure 5
Figure 5
Accuracy plotted against the GO term depth for the molecular function namespace. Presented are average AUC values for three of the predictors in Table 3. Term depth is computed as the length of the longest path to the root of the ontology. The labels "Cross-sp.", "Sp.-Spec.", and "M. View" refer to the cross-species, species-specific and multi-view predictors, respectively.
Figure 6
Figure 6
Accuracy plotted against the GO term depth for the biological process namespace. Presented are average AUC values for three of the predictors in Table 3. Term depth is computed as the length of the longest path to the root of the ontology. The labels "Cross-sp.", "Sp.-Spec.", and "M. View" are the same as above.
Figure 7
Figure 7
Accuracy plotted against the GO term depth for the cellular component namespace. Presented are average AUC values for three of the predictors in Table 3. Term depth is computed as the length of the longest path to the root of the ontology. The labels "Cross-sp.", "Sp.-Spec.", and "M. View" are the same as above.
See this image and copyright information in PMC

References

    1. Sokolov A, Ben-Hur A. Hierarchical classification of Gene Ontology terms using the GOstruct method. Journal of Bioinformatics and Compuational Biology. 2010;8(2):357–376. doi: 10.1142/S0219720010004744. - DOI - PubMed
    1. Blum A, Mitchell T. Proceedings of the eleventh annual conference on Computational learning theory. ACM; 1998. Combining labeled and unlabeled data with co-training; p. 100.
    1. Sokolov A, Ben-Hur A. Multi-view prediction of protein function. ACM Conference on Bioinformatics, Computational Biology and Biomedicine. 2011.
    1. Gene Ontology Consortium. Gene ontology: tool for the unification of biology. Nat Genet. 2000. pp. 25–9. - PMC - PubMed
    1. Barutcuoglu Z, Schapire R, Troyanskaya O. Hierarchical multi-label prediction of gene function. Bioinformatics. 2006;22(7):830. doi: 10.1093/bioinformatics/btk048. - DOI - PubMed

Publication types