Probabilistic clustering for hierarchical multi-label classification of protein functions

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

Volumn 8189 LNAI, Issue PART 2, 2013, Pages 385-400

  Barros, Rodrigo C ^a   Cerri, Ricardo ^a   Freitas, Alex A ^b   De Carvalho, André C P L F ^a  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

^b UNIVERSITY OF KENT   (United Kingdom)

Author keywords

Hierarchical Multi Label Classification; Probabilistic Clustering; Protein Function Prediction

Indexed keywords

CLUSTER MEMBERSHIPS; DIRECTED ACYCLIC GRAPH (DAG); EMPIRICAL ANALYSIS; HIERARCHICAL MULTI-LABEL CLASSIFICATIONS; PROBABILISTIC CLUSTERING; PROTEIN FUNCTION PREDICTION; PROTEIN FUNCTIONS; STATE-OF-THE-ART METHODS;

LEARNING ALGORITHMS; LEARNING SYSTEMS; PROTEINS;

CLASSIFICATION (OF INFORMATION);

EID: 84886544524     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40991-2_25     Document Type: Conference Paper

References (31)

1. Ahmed, M.S.: Clustering guided multi-label text classification. Ph.D. thesis, University of Texas at Dallas (2012)
2. Aleksovski, D., Kocev, D., Dzeroski, S.: Evaluation of distance measures for hierarchical multilabel classification in functional genomics. In: 1st Workshop on Learning from Multi-Label Data (MLD) held in conjunction with ECML/PKDD, pp. 5-16 (2009)
3. Ashburner, M., et al.: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature Genetics 25, 25-29 (2000)
4. Barutcuoglu, Z., Schapire, R.E., Troyanskaya, O.G.: Hierarchical multi-label prediction of gene function. Bioinformatics 22, 830-836 (2006)
5. Blockeel, H., Bruynooghe, M., Dzeroski, S., Ramon, J., Struyf, J.: Hierarchical multi-classification. In: Workshop on Multi-Relational Data Mining, pp. 21-35 (2002)
6. Blockeel, H., Schietgat, L., Struyf, J., Džeroski, S., Clare, A.J.: Decision trees for hierarchical multilabel classification: A case study in functional genomics. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 18-29. Springer, Heidelberg (2006)
7. Cerri, R., Barros, R.C., Carvalho, A.C.P.L.F.: Hierarchical multi-label classification for protein function prediction: A local approach based on neural networks. In: Intelligent Systems Design and Applications (ISDA), pp. 337-343 (November 2011)
8. Cerri, R., Barros, R.C., Carvalho, A.C.P.L.F.: A genetic algorithm for hierarchical multi-label classification. In: Proceedings of the 27th Annual ACM Symposium on Applied Computing, SAC 2012, pp. 250-255. ACM, New York (2012)
9. Cerri, R., Barros, R.C., Carvalho, A.C.P.L.F.: Hierarchical multi-label classification using local neural networks. Journal of Computer and System Sciences (in press, 2013)
10. Clare, A., King, R.D.: Predicting gene function in saccharomyces cerevisiae. Bioinformatics 19, 42-49 (2003)
11. Costa, E.P., Lorena, A.C., Carvalho, A.C.P.L.F., Freitas, A.A., Holden, N.: Comparing several approaches for hierarchical classification of proteins with decision trees. In: Sagot, M.-F., Walter, M.E.M.T. (eds.) BSB 2007. LNCS (LNBI), vol. 4643, pp. 126-137. Springer, Heidelberg (2007)
12. Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: International Conference on Machine Learning, pp. 233-240 (2006)
13. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society 39(1), 1-38 (1977)
14. Dorigo, M.: Optimization, Learning and Natural Algorithms. Ph.D. thesis, Dipartimento di Elettronica, Politecnico di Milano, IT (1992)
15. Dorigo, M., Maniezzo, V., Colorni, A.: Positive feedback as a search strategy. Tech. rep., Dipartimento di Elettronica, Politecnico di Milano, IT (1991)
16. Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning 29(2-3), 131-163 (1997)
17. García, S., Fernández, A., Luengo, J., Herrera, F.: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Information Sciences 180(10), 2044-2064 (2010)
18. Kiritchenko, S., Matwin, S., Famili, A.: 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)
19. Kiritchenko, S., Matwin, S., Nock, R., Famili, A.: Learning and evaluation in the presence of class hierarchies: Application to text categorization. In: Lamontagne, L., Marchand, M. (eds.) Canadian AI 2006. LNCS (LNAI), vol. 4013, pp. 395-406. Springer, Heidelberg (2006)
20. Lloyd, S.P.: Least squares quantization in pcm. IEEE Transactions on Information Theory 28(2), 129-137 (1982)
21. Nasierding, G., Tsoumakas, G., Kouzani, A.Z.: Clustering based multi-label classification for image annotation and retrieval. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 4514-4519 (2009)
22. Otero, F.E.B., Freitas, A.A., Johnson, C.: A hierarchical classification ant colony algorithm for predicting gene ontology terms. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds.) EvoBIO 2009. LNCS, vol. 5483, pp. 68-79. Springer, Heidelberg (2009)
23. Otero, F.E.B., Freitas, A.A., Johnson, C.: A hierarchical multi-label classification ant colony algorithm for protein function prediction. Memetic Computing 2, 165-181 (2010)
24. Quinlan, J.R.: C4.5: programs for machine learning. Kaufmann Publishers Inc., San Francisco (1993)
25. Rousu, J., Saunders, C., Szedmak, S., Shawe-Taylor, J.: Kernel-based learning of hierarchical multilabel classification models. Journal of Machine Learning Research 7, 1601-1626 (2006)
26. Ruepp, A., Zollner, A., Maier, D., Albermann, K., Hani, J., Mokrejs, M., Tetko, I., Güldener, U., Mannhaupt, G., Münsterkötter, M., Mewes, H.W.: The funcat, a functional annotation scheme for systematic classification of proteins from whole genomes. Nucleic Acids Research 32(18), 5539-5545 (2004)
27. Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. In: Machine Learning, vol. 37, pp. 297-336. Kluwer Academic Publishers, Hingham (1999)
28. Silla, C., Freitas, A.A.: A survey of hierarchical classification across different application domains. Data Mining and Knowledge Discovery 22, 31-72 (2011)
29. Sun, A., Lim, E.P.: Hierarchical text classification and evaluation. In: Fourth IEEE International Conference on Data Mining, pp. 521-528 (2001)
30. Sun, A., Lim, E.P., Ng, W.K., Srivastava, J.: Blocking Reduction Strategies in Hierarchical Text Classification. IEEE Transactions on Knowledge and Data Engineering 16, 1305-1308 (2004)
31. Vens, C., Struyf, J., Schietgat, L., Džeroski, S., Blockeel, H.: Decision trees for hierarchical multi-label classification. Machine Learning 73, 185-214 (2008)