Transferring network topological knowledge for predicting protein-protein interactions

Proteomics

Volumn 11, Issue 19, 2011, Pages 3818-3825

  Xu, Qian ^a   Xiang, Evan Wei ^a   Yang, Qiang ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

Bioinformatics; Collective matrix factorization; Protein protein interactions; Transfer learning

Indexed keywords

AREA UNDER THE CURVE; ARTICLE; BIOINFORMATICS; HELICOBACTER PYLORI; K NEAREST NEIGHBOR; MACHINE LEARNING; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN PROTEIN INTERACTION; PROTEIN STRUCTURE; PROTEOMICS; RECEIVER OPERATING CHARACTERISTIC; SUPPORT VECTOR MACHINE;

HELICOBACTER PYLORI;

EID: 80053022680     PISSN: 16159853     EISSN: 16159861     Source Type: Journal    
DOI: 10.1002/pmic.201100146     Document Type: Article

References (34)

1. Yu, J., Fotouhi, F., Computational approaches for predicting protein-protein interactions: a survey. J. Med. Syst. 2006, 30, 39-44.
2. Mrowka, R., Patzak, A., Herzel, H., Is there a bias in proteome research? Genome Res. 2001, 11, 1971-1973.
3. Urquiza, J. M., Rojas, I., Pomares, H., Florido J. P. et al., Method for prediction of protein-protein interactions in yeast using genomics/proteomics information and feature selection. Proceedings of the 10th International Work - Conference on Artificial Neural Networks: Part I: Bio-inspired Systems: Computational and Ambient Intelligence, 2009, 853-860.
4. Singh, A. P., Gordon, G. J., Relational learning via collective matrix factorization. Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008, 650-658.
5. Gomez, S., Noble, W., Rzhetsky, A., Learning to predict protein-protein interactions from protein sequences. Bioinformatics 2003, 19, 1875-1881.
6. Deng, M., Mehta, S., Sun, F., Chen, T., Inferring domain-domain interactions from protein-protein interactions. Genome Res. 2002, 12, 1540-1548.
7. Wang, H., Segal, E., Ben-Hur, A., Koller, D. et al., Identifying protein-protein interaction sites on a genome-wide scale. In: Advances in Neural Information Processing Systems, 2005, 17, 1465-1472.
8. Wang, H., Segal, E., Ben-Hur, A., Li, Q. et al., Insite: a computational method for identifying protein-protein interaction binding sites on a proteome-wide scale. Genome Biol. 2007, 8, 1-18.
9. Li, M., Lin, L., Wang, X., Liu, T., Protein-protein interaction site prediction based on conditional random fields. Bioinformatics 2007, 23, 597-604.
10. Wu, S., Zhang, Y., A comprehensive assessment of sequence-based and template-based methods for protein contact prediction. Bioinformatics 2008, 24, 24-931.
11. Bock, J., Gough, D., Predicting protein-protein interactions from primary structure. Bioinformatics 2001, 17, 455-460.
12. Espadaler, J., Romero-Isart, O., Jackson, R. M., Oliva, B., Prediction of protein-protein interactions using distant conservation of sequence patterns and structure relationships. Bioinformatics 2005, 21, 3360-3368.
13. von Mering, C., Krause, R., Snel, B., Cornell, M. et al., Comparative assessment of large-scale data sets of protein-protein interactions. Nature 2002, 417, 399-403.
14. Qi, Y., Klein-Seetharaman, J., Bar-Joseph, Z., A mixture of feature experts approach for protein-protein interaction prediction. BMC Bioinformatics 2007, 8, 6.
15. Arkin, A., Synthetic cell biology. Curr. Opin. Biotech. 2001, 12, 638-644.
16. Shoemaker, B., Panchenko, A., Deciphering protein-protein interactions. Part II. Computational methods to predict protein and domain interaction partners. PLoS Comput. Biol. 2007, 3, e43.
17. Matthews, L. R., Vaglio, P., Reboul, J., Ge, H. et al., Identification of potential interaction networks using sequence-based searches for conserved protein-protein interactions or "interologs". Genome Res. 2001, 11, 2120-2126.
18. Wojcik, J. J., Schchter, V., Protein-protein interaction map inference using interacting domain profile pairs. Bioinformatics 2001, 17, 296-305.
19. Cao, B., Liu, N., Yang, Q., Transfer learning for collective link prediction in multiple heterogeneous domains. Proceedings of 27th International Conference on Machine Learning, 2010, 159-166.
20. Pan, W., Xiang, E., Liu, N., Yang, Q., Transfer learning in collaborative filtering for sparsity reduction. Proceedings of the 24th AAAI Conference on Artificial Intelligence, 2010, 230-235.
21. Li, B., Yang, Q., Xue, X., Can movies and books collaborate? Cross-domain collaborative filtering for sparsity reduction. Proceedings of the Twenty-first International Joint Conference on Artificial Intelligence, 2009, 2052-2057.
22. Zhou, D., Bousquet, O., Lal, T. N., Weston, J. et al., Learning with local and global consistency. In: Thrun, S., Saul, L. K., Schölkopf, B., (Eds), Advances in Neural Information Processing Systems, vol. 16, MIT Press, Cambridge, MA, USA 2004, pp. 321-328.
23. Kashima, H., Yamanishi, Y., Kato, T., Sugiyama, M. et al., Simultaneous inference of biological networks of multiple species from genome-wide data and evolutionary information: a semi-supervised approach. Bioinformatics 2009, 5, 2962-2968.
24. Singh A. P., Gordon, G. J., A unified view of matrix factorization models. Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases - Part II, 2008, 5212, 358-373.
25. Singh, R., Xu, J., Berger, B., Global alignment of multiple protein interaction networks with application to functional orthology detection. Proc. Natl. Acad. Sci. USA 2008, 105, 12763-12768.
26. Smith, T., Waterman, M., Identification of common molecular subsequences. J. Mol. Biol. 1981, 147, 95-197.
27. Bock, J., Gough, D., Whole-proteome interaction mining. Bioinformatics 2003, 19, 125-135.
28. Martin, S., Roe, D., Faulon, J. L., Predicting protein-protein interactions using signature products. Bioinformatics 2005, 21, 218-226.
29. Nanni, L., Fusion of classifiers for predicting protein-protein interactions. Neurocomputing 2005, 68, 289-296.
30. Nanni, L., Hyperplanes for predicting protein-protein interactions. Neurocomputing 2005, 69, 257-263.
31. Nanni, L., Lumini, A., An ensemble of k-local hyperplanes for predicting protein-protein interactions. Bioinformatics 2006, 22, 1207-1210.
32. Fawcett, T., An introduction to roc analysis. Pattern Recog. Lett. 2006, 27, 861-874.
33. Culver, M. G., Active learning to maximize area under the roc curve. Proceedings of the 6th International Conference on Data Mining, 2006, 149-158.
34. Liu, L., Cai, Y., Lu, W., Feng, K. et al., Prediction of protein-protein interactions based on pseaa composition and hybrid feature selection. Biochem. Biophys. Res. Commun. 2009, 380, 318-322.