Network Assessor: An automated method for quantitative assessment of a network's potential for gene function prediction

Frontiers in Genetics

Volumn 5, Issue MAY, 2014, Pages

  Montojo, Jason ^a   Zuberi, Khalid ^a   Shao, Quentin ^a   Bader, Gary D ^a   Morris, Quaid ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

Cross validation; Function prediction; Machine learning; Network biology; Network inference

Indexed keywords

EID: 84905640748     PISSN: None     EISSN: 16648021     Source Type: Journal    
DOI: 10.3389/fgene.2014.00123     Document Type: Article

References (36)

1. Barrett, T., Wilhite, S. E., Ledoux, P., Evangelista, C., Kim, I. F., Tomashevsky, M., et al. (2013). NCBI GEO: archive for functional genomics data sets-update. Nucleic Acids Res. 41, D991-5. doi: 10.1093/nar/gks1193
2. Brown, K. R., and Jurisica, I. (2007). Unequal evolutionary conservation of human protein interactions in interologous networks. Genome Biol. 8, R95. doi: 10.1186/gb-2007-8-5-r95
3. Cerami, E. G., Gross, B. E., Demir, E., Rodchenkov, I., Babur, ö., Anwar, N., et al. (2011). Pathway Commons, a web resource for biological pathway data. Nucl. Acids Res. 39(Suppl. 1), D685-D690. doi: 10.1093/nar/gkq1039
4. Chatr-Aryamontri, A., Breitkreutz, B. J., Heinicke, S., Boucher, L., Winter, A., Stark, C., et al. (2013). The BioGRID interaction database: 2013 update. Nucleic Acids Res. 41, D816-D823. doi: 10.1093/nar/gks1158
5. Costanzo, M., Baryshnikova, A., Bellay, J., Kim, Y., Spear, E. D., Sevier, C. S., et al. (2010). The genetic landscape of a cell. Science 327, 425-431. doi: 10.1126/science.1180823
6. Deng, M., Tu, Z., Sun, F., and Chen, T. (2004). Mapping Gene Ontology to proteins based on protein-protein interaction data. Bioinformatics 20, 895-902. doi: 10.1093/bioinformatics/btg500
7. Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recogn. Lett. 27, 861-874. doi: 10.1016/j.patrec.2005.10.010
8. Flicek, P., Ahmed, I., Amode, M. R., Barrell, D., Beal, K., Brent, S., et al. (2013). Ensembl 2013. Nucl. Acids Res. 41, D48-D55. doi: 10.1093/nar/gks1236
9. Franceschini, A., Szklarczyk, D., Frankild, S., Kuhn, M., Simonovic, M., Roth, A., et al. (2013). STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41, D808-15. doi: 10.1093/nar/gks1094
10. Gillis, J., and Pavlidis, P. (2011). The impact of multifunctional genes on "guilt by association" analysis. PLoS ONE 6:e17258. doi: 10.1371/journal.pone.0017258
11. Gillis, J., and Pavlidis, P. (2012). "Guilt by association" is the exception rather than the rule in gene networks. PLoS Comput. Biol. 8:e1002444. doi: 10.1371/journal.pcbi.1002444
12. Hishigaki, H., Nakai, K., Ono, T., Tanigami, A., and Takagi, T. (2001). Assessment of prediction accuracy of protein function from protein-protein interaction data. Yeast 18, 523-531. doi: 10.1002/yea.706
13. Hunter, S., Jones, P., Mitchell, A., Apweiler, R., Attwood, T. K., Bateman, A., et al. (2012). InterPro in 2011: new developments in the family and domain prediction database. Nucleic Acids Res. 40, D306-D312. doi: 10.1093/nar/gkr948
14. Kato, T., Kashima, H., and Sugiyama, M. (2009). Robust label propagation on multiple networks. IEEE Trans. Neural Netw. 20, 35-44. doi: 10.1109/TNN.2008.2003354
15. Kleessen, S., Klie, S., and Nikoloski, Z. (2013). Data integration through proximity-based networks provides biological principles of organization across scales. Plant Cell 25, 1917-1927. doi: 10.1105/tpc.113.111039
16. Klie, S., Mutwil, M., Persson, S., and Nikoloski, Z. (2012). Inferring gene functions through dissection of relevance networks: interleaving the intra-and inter-species views. Mol. Biosyst. 8, 2233-2241. doi: 10.1039/c2mb25089f
17. Michaut, M., and Bader, G. D. (2012). Multiple genetic interaction experiments provide complementary information useful for gene function prediction. PLoS Comput. Biol. 8:e1002559. doi: 10.1371/journal.pcbi.1002559
18. Mostafavi, S., and Morris, Q. (2010). Fast integration of heterogeneous data sources for predicting gene function with limited annotation. Bioinformatics 26, 1759-1765. doi: 10.1093/bioinformatics/btq262
19. Mostafavi, S., Ray, D., Warde-Farley, D., Grouios, C., and Morris, Q. (2008). GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. Genome Biol. 9(Suppl. 1), S4. doi: 10.1186/gb-2008-9-s1-s4
20. Peña-Castillo, L., Tasan, M., Meyers, C. L., Lee, H., Joshi, T., Zhang, C., et al. (2008). A critical assessment of Mus musculus gene function prediction using integrated genomic evidence. Genome Biol. 9(Suppl. 1), S2. doi: 10.1186/gb-2008-9-s1-s2
21. Punta, M., Coggill, P. C., Eberhardt, R. Y., Mistry, J., Tate, J., Boursnell, C., et al. (2012). The Pfam protein families database. Nucleic Acids Res. 40, D290-D301. doi: 10.1093/nar/gkr1065
22. Razick, S., Magklaras, G., and Donaldson, I. M. (2008). iRefIndex: a consolidated protein interaction database with provenance. BMC Bioinformatics 9:405 doi: 10.1186/1471-2105-9-405
23. Ruepp, A., Zollner, A., Maier, D., Albermann, K., Hani, J., Mokrejs, M., et al. (2004). The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes. Nucleic Acids Res. 32, 5539-5545. doi: 10.1093/nar/gkh894
24. Schrynemackers, M., Küffner, R., and Geurts, P. (2013). On protocols and measures for the validation of supervised methods for the inference of biological networks. Front. Genet. 4:262. doi: 10.3389/fgene.2013.00262
25. Schwikowski, B., Uetz, P., and Fields, S. (2000). A network of protein-protein interactions in yeast. Nat. Biotechnol. 18, 1257-1261. doi: 10.1038/82360
26. Sharan, R., Ulitsky, I., and Shamir, R. (2007). Network-based prediction of protein function. Mol Sys Biol 3, 88. doi: 10.1038/msb4100129
27. The Gene Ontology Consortium. (2000). Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25-29. doi: 10.1038/75556
28. Tsoumakas, G., Katakis, I., and Vlahavas, I. (2010). "Mining multi-label data," in Data Mining and Knowledge Discovery Handbook, eds O. Maimon and L. Rokach (New York, NY: Springer), 667-685.
29. Vazquez, A., Flammini, A., Maritan, A., and Vespignani, A. (2003). Global protein function prediction from protein-protein interaction networks. Nat. Biotechnol. 21, 697-700. doi: 10.1038/nbt825
30. Wang, H., Huang, H., and Ding, C. (2013). Function-function correlated multi-label protein function prediction over interaction networks. J. Comput. Biol. 20, 322-343. doi: 10.1089/cmb.2012.0272
31. Warde-Farley, D., Donaldson, S. L., Comes, O., Zuberi, K., Badrawi, R., Chao, P., et al. (2010). The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucl. Acids Res. 38(Suppl. 2), W214-W220. doi: 10.1093/nar/gkq537
32. Wu, L. F., Hughes, T. R., Davierwala, A. P., Robinson, M. D., Stoughton, R., and Altschuler, S. J. (2002). Large-scale prediction of Saccharomyces cerevisiae gene function using overlapping transcriptional clusters. Nat. Genet. 31, 255-265. doi: 10.1038/ng906
33. Ye, P., Peyser, B. D., Pan, X., Boeke, J. D., Spencer, F. A., and Bader, J. S. (2005). Gene function prediction from congruent synthetic lethal interactions in yeast. Mol. Syst. Biol. 1, 2005.0026. doi: 10.1038/msb4100034
34. Yu, G., Rangwala, H., Domeniconi, C., Zhang, G., and Yu, Z. (2013). Protein function prediction using multi-label ensemble classification. EEE/ACM Trans. Comput. Biol. Bioinform. doi: 10.1109/TCBB.2013.111. [Epub ahead of print].
35. Zhang, W., Morris, Q. D., Chang, R., Shai, O., Bakowski, M. A., Mitsakakis, N., et al. (2004). The functional landscape of mouse gene expression. J. Biol. 3, 21. doi: 10.1186/jbiol16
36. Zuberi, K., Franz, M., Rodriguez, H., Montojo, J., Lopes, C. T., Bader, G. D., et al. (2013). GeneMANIA prediction server 2013 update. Nucl. Acids Res. 41, W115-W122. doi: 10.1093/nar/gkt533