A global-model naive Bayes approach to the hierarchical prediction of protein functions

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2009, Pages 992-997

  Silla Jr , Carlos N ^a   Freitas, Alex A ^a  

^a UNIVERSITY OF KENT   (United Kingdom)

Author keywords

Bayesian classification; Hierarchical classification; Protein function prediction

Indexed keywords

BAYESIAN CLASSIFICATION; CLASSIFICATION ALGORITHM; CLASSIFICATION MODELS; GLOBAL MODELS; HIERARCHICAL CLASSIFICATION; ITS EVALUATION; LOCAL MODEL; MODEL APPROACH; NAIVE BAYES; NAIVE BAYES APPROACHES; ORIGINAL ALGORITHMS; PREDICTION OF PROTEIN FUNCTION; PROTEIN FUNCTION PREDICTION; PROTEIN FUNCTIONS;

BAYESIAN NETWORKS; CLASSIFICATION (OF INFORMATION); FORECASTING; PROTEINS;

DATA MINING;

EID: 77951199022     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2009.85     Document Type: Conference Paper

References (18)

1. D. Tikk, G. Biró, and J. D. Yang, "A hierarchical text categorization approach and its application to frt expansion," Australian Journal of Intelligent Information Processing Systems, vol. 8, no. 3, pp. 123-131, 2004.
2. D. W. Corne and G. B. Fogel, Evolutionary Computation in Bioinformatics. Morgan Kaufmann, 2002, ch. An Introduction to Bioinformatics for Computer Scientists, pp. 3-18.
3. J. A. Gerlt and P. C. Babbitt, "Can sequence determine function?" Genome Biology, vol. 1, no. 5, 2000.
4. A. A. Freitas and A. C. P. L. F. de Carvalho, Research and Trends in Data Mining Technologies and Applications. Idea Group, 2007, ch. A Tutorial on Hierarchical Classification with Applications in Bioinformatics, pp. 175-208.
5. A. Sun and E.-P. Lim, "Hierarchical text classification and evaluation," in Proc. of the IEEE Int. Conf. on Data Mining, 2001, pp. 521-528.
6. D. Koller and M. Sahami, "Hierarchically classifying documents using very few words," in Proc. of the 14th Int. Conf. on Machine Learning, 1997, pp. 170-178.
7. S. D́ Alessio, K. Murray, R. Schiaffino, and A. Kershenbaum, "The effect of using hierarchical classifiers in text categorization," in Proc. of the 6th Int. Conf. Recherche d́ Information Assistee par Ordinateur, 2000, pp. 302-313.
8. E. Costa, A. Lorena, A. Carvalho, A. A. Freitas, and N. Holden., "Comparing several approaches for hierarchical classification of proteins with decision trees," in Advances in Bioinformatics and Computational Biology, ser. Lecture Notes in Bioinformatics, vol. 4643. Springer, 2007, pp. 126- 137.
9. C. Vens, J. Struyf, L. Schietgat, S. so Dz̃eroski, and H. Blockeel, "Decision trees for hierarchical multi-label classification," Machine Learning, vol. 73, no. 2, pp. 185-214, 2008.
10. D. Heckerman, "A tutorial on learning with bayesian networks," Microsoft, Technical Report MSR-TR-95-06, 1995.
11. T. M. Mitchell, Machine Learning. McGraw-Hill, 1997.
12. A. Clare, "Machine learning and data mining for yeast functional genomics," Ph.D. dissertation, University of Wales Aberystwyth, 2004.
13. D. Filmore, "It's a GPCR world," Modern drug discovery, vol. 7, no. 11, pp. 24-28, 2004.
14. N. Holden and A. A. Freitas, "Hierarchical classification of protein function with ensembles of rules and particle swarm optimisation," Soft Computing Journal, vol. 13, pp. 259-272, 2009.
15. S. Kiritchenko, S. Matwin, and A. F. Famili, "Functional annotation of genes using hierarchical text categorization," in Proc. of the ACL Workshop on Linking Biological Literature, Ontologies and Databases: Mining Biological Semantics, 2005.
16. S. García and F. Herrera, "An extension on "statistical comparisons of classifiers over multiple data sets" for all pairwise comparisons," Journal of Machine Learning Research, vol. 9, pp. 2677-2694, 2008.
17. M. Ceci and D. Malerba, "Classifying web documents in a hierarchy of categories: A comprehensive study," Journal of Intelligent Information Systems, vol. 28, no. 1, pp. 1-41, 2007.
18. J. Rousu, C. Saunders, S. Szedmak, and J. Shawe-Taylor, "Kernel-based learning of hierarchical multilabel classification models," Journal of Machine Learning Research, vol. 7, pp. 1601-1626, 2006.