Handling missing features with boosting algorithms for protein-protein interaction prediction

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6254 LNBI, Issue , 2010, Pages 132-147

  Smeraldi, Fabrizio ^a   Defoin Platel, Michael ^b   Saqi, Mansoor ^b  

^a QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

^b ROTHAMSTED RESEARCH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ADABOOST; BAYESIAN CLASSIFIER; BAYESIAN METHODS; BOOSTING ALGORITHM; DATA SETS; DENSITY ESTIMATION; FEATURE SELECTION AND CLASSIFICATION; FEATURE VECTORS; MISSING DATA; MISSING VALUES; MULTIPLE HETEROGENEOUS DATA SOURCE; PREDICTION ACCURACY; PROTEIN-PROTEIN INTERACTIONS;

ADAPTIVE BOOSTING; BAYESIAN NETWORKS; DATA HANDLING; FORECASTING; PROTEINS;

FEATURE EXTRACTION;

EID: 78049420687     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15120-0_11     Document Type: Conference Paper

References (26)

1. Azuaje, F., Dopazo, J.: Data Analysis and Visualization in Genomics and Proteomics. John Wiley & Sons, Chichester (2005)
2. Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Machine Learning 36(1-2), 105-139 (1999)
3. Ben-Hur, A., Noble, W.S.: Kernel methods for predicting protein-protein interactions. Bioinformatics 21(Suppl. 1), i38-i46 (2005) (Pubitemid 41794473)
4. Bork, P., Jensen, L.J., von Mering, C., Ramani, A.K., Lee, I., Marcotte, E.M.: Protein interaction networks from yeast to human. Curr. Opin. Struct. Biol. 14(3), 292-299 (2004)
5. Breitkreutz, B.J., Stark, C., Reguly, T., Boucher, L., Breitkreutz, A., Livstone, M., Oughtred, R., Lackner, D.H., Bhler, J., Wood, V., Dolinski, K., Tyers, M.: The bioGRID interaction database: 2008 update. Nucleic Acids Res. 36(Database issue), D637-D640 (2008)
6. Deane, C.M., Salwiski, L., Xenarios, I., Eisenberg, D.: Protein interactions: two methods for assessment of the reliability of high throughput observations. Mol. Cell. Proteomics 1(5), 349-356 (2002)
7. Edwards, A.M., Kus, B., Jansen, R., Greenbaum, D., Greenblatt, J., Gerstein, M.: Bridging structural biology and genomics: assessing protein interaction data with known complexes. Trends Genet. 18(10), 529-536 (2002)
8. Freund, Y., Schapire, R.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Science 55(1) (1997)
9. Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning. Springer, Heidelberg (2001)
10. Jansen, R., Greenbaum, D., Gerstein, M.: Relating whole-genome expression data with protein-protein interactions. Genome Res. 12(1), 37-46 (2002) (Pubitemid 34065588)
11. Jansen, R., Yu, H., Greenbaum, D., Kluger, Y., Krogan, N.J., Chung, S., Emili, A., Snyder, M., Greenblatt, J.F., Gerstein, M.: A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 302(5644), 449-453 (2003)
12. Kerrien, S., Alam-Faruque, Y., Aranda, B., Bancarz, I., Bridge, A., Derow, C., Dimmer, E., Feuermann, M., Friedrichsen, A., Huntley, R., Kohler, C., Khadake, J., Leroy, C., Liban, A., Lieftink, C., Montecchi-Palazzi, L., Orchard, S., Risse, J., Robbe, K., Roechert, B., Thorneycroft, D., Zhang, Y., Apweiler, R., Hermjakob, H.: Intact-open source resource for molecular interaction data. Nucleic Acids Res. 35(Database issue), D561-D565 (2007)
13. Lin, M., Hu, B., Chen, L., Sun, P., Fan, Y., Wu, P., Chen, X.: Computational identification of potential molecular interactions in Arabidopsis. Plant Physiol. 151(1), 34-46 (2009)
14. Lin, N., Wu, B., Jansen, R., Gerstein, M., Zhao, H.: Information assessment on predicting protein-protein interactions. BMC Bioinformatics 5, 154 (2004)
15. Lu, L.J., Xia, Y., Paccanaro, A., Yu, H., Gerstein, M.: Assessing the limits of genomic data integration for predicting protein networks. Genome Res. 15(7), 945-953 (2005)
16. Malacaria, P., Smeraldi, F.: On Adaboost and optimal betting strategies. In: Proceedings of the 5th international conference on data mining (dmin/worldcomp), July 2009, pp. 326-332. CSREA Press (2009)
17. von Mering, C., Krause, R., Snel, B., Cornell, M., Oliver, S.G., Fields, S., Bork, P.: Comparative assessment of large-scale data sets of protein-protein interactions. Nature 417(6887), 399-403 (2002) (Pubitemid 34563526)
18. Michalski, R.S., Carbonell, J.G., Mitchell, T.M.: Machine Learning: An Artificial Intelligence Approach. Tioga Publishing Company (1983)
19. Najafabadi, H.S., Salavati, R.: Sequence-based prediction of protein-protein interaction by means of codon usage. Genome Biology 9(5) (2008)
20. Pelckmans, K., Brabanter, J.D., Suykens, J.A.K., Moor, B.D.: Handling missing values in support vector machine classifiers. Neural Networks 18, 684-692 (2005)
21. Rätsch, G., Warmuth, M.: Efficient margin maximizing with boosting. Journal of Machine Learning Research 6, 2131-2152 (2005)
22. Rudin, C., Schapire, R.E., Daubechies, I.: On the dynamics of boosting. In: Advances in Neural Information Processing Systems, vol. 16 (2004)
23. Schapire, R., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Machine Learning 37(3) (1999)
24. Scott, M.S., Barton, G.J.: Probabilistic prediction and ranking of human proteinprotein interactions. BMC Bioinformatics 8, 239 (2007)
25. Shen, J., Zhang, J., Luo, X., Zhu,W., Yu, K., Chen, K., Li, Y., Jiang, H.: Predicting protein-protein interactions based only on sequences information. Proc. Natl. Acad. Sci. U.S.A. 104(11), 4337-4341 (2007)
26. Vapnik, V.N.: The nature of statistical learning theory. Springer, Heidelberg (1995)