Multi-Label Hierarchical Classification for Protein Function Prediction
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32807
Multi-Label Hierarchical Classification for Protein Function Prediction

Authors: Helyane B. Borges, Julio Cesar Nievola

Abstract:

Hierarchical classification is a problem with applications in many areas as protein function prediction where the dates are hierarchically structured. Therefore, it is necessary the development of algorithms able to induce hierarchical classification models. This paper presents experimenters using the algorithm for hierarchical classification called Multi-label Hierarchical Classification using a Competitive Neural Network (MHC-CNN). It was tested in ten datasets the Gene Ontology (GO) Cellular Component Domain. The results are compared with the Clus-HMC and Clus-HSC using the hF-Measure.

Keywords: Hierarchical Classification, Competitive Neural Network, Global Classifier.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1086573

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2338

References:


[1] A. Sum; E. Lim. Hierarchical Text Classification and Evaluation. In Proceedings of the International Conference on Data Mining (ICDM 2001), California, USA, Nov, 2001. p. 521-528.
[2] A. Freitas; A. C. Carvalho. "A Tutorial on Hierarchical Classification with Applications in Bioinformatics". In Research and Trends in Data Mining Technologies and Applications, chapter VII, pp. 175-208. Idea Group, 2007.
[3] C. Vens, J. Struyf, L. Schietgat, S. D'zeroski e H. Blockeel. Decision trees for hierarchical multi-label classification. Machine Learning, vol. 73, pp. 185-214, 2008.
[4] C. Silla and A. A. Freitas. A survey of hierarchical classification across different application domains. Data Mining and Knowledge Discovery. Abr, 2010.
[5] H. Blockeel, M. Bruynooghe, S. Dzeroski, J. Ramon e J. Struyf. Hierarchical multi-classification. In Workshop on Multi-Relational Data Mining, pp. 21-35, 2002.
[6] N. Holden; A. A. Freitas. A hybrid particle swarm/ant colony algorithm for the classification of hierarchical biological data. In Swarm Inteligence Symposium, 2005 Proceedings 2005 IEEE, p 100-107.
[7] R. T. Alves; M. R. Delgado; A. A. Freitas. A. A. Multi-label hierarchical classification of protein functions with artificial immune systems. In Proc. Advances in Bioinformatics and Computational Biology, 2008 v. 5167, p.1-12.
[8] S. Haykin. Redes neurais: principios e prkica. 2.ed. Traducao de, Paulo Martins Engel. Porto Alegre: Bookman, 2001.
[9] S. Kiritchenko; S. Matwin; F. Famili. Hierarchical Text Categorization as a Tool of Associating Genes with Gene Ontology Codes. In European Workshop on Data Mining and Text Mining in Bioinformatics, pp. 30¬34, 2004.
[10] S. Kiritchenko; S. Matwin; F. Famili; R. Nock. Learning and evaluation in the presence of class hierarchies: Application to text categorization. In Proc. of the 19th Canadian Conf. on Artificial Intelligence, Lecture Notes in Artificial Intelligence, 2006. v. 4013, p. 395-406.
[11] T. Kohonen. The Self-Organizing Map. Proceedings of IEEE. v.78, n.9. p-1464-1480. 1990.
[12] Y. Guan et al. Predicting gene function in a hierarchical context with an ensemble of classifiers. Genome Biology, v. 9, p. 1-18. June, 2008.
[13] Barutcuoglu, Z., Schapire, R. E. & Troyanskaya, 0. G. Hierarchical multi-label prediction of gene function. Bioinformatics. v. 22 n. 7, p. 830-836.2006.
[14] M. Friedman. 1937. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, v. 32, p. 675-701.
[15] M. Friedman. 1940. A comparison of alternative tests of significance for the problem of m rankings. In: Annals of Mathematical Statistics. v. 11, p. 86-92.
[16] K. Tu; H. Yu; Z. Guo; X. Li. Learnability-based further prediction of gene functions in Gene Ontology. Genomics. v. 84, p. 922-928. 2004.
[17] Jensen, L. J. et al. 2003. Prediction of human protein function according to Gene Ontology categories. Bioinformatics. v. 19, n. 5, p. 635-642.
[18] A. Clare; R. D. King. Knowledge discovery in multi-label phenotype data. In Proceedings of the 5th European Conference on Principles and Practice of Knowledge Discovery and Data Mining (PKDD-2001), Freiburg, Germany. p. 42-53, 2001.
[19] A. Clare; R. D. King. "Predicting gene function in Saccharomyces cerevisiae". Bioinformatics, vol. 19, pp. 42-49, 2003.
[20] B. Jin; B. Muller; Zhai. C; Lu. X. Multi-label literature classification based on the gene ontology graph. BMC Bioinformatics. v. 9, n. 525, p. 1-15, 2008.
[21] D. Koller; M. Sahami. Hierarchically classifying documents using very few words. In Proc. of the 14th Int. Conf. on Machine Learning (ICML 1997), San Francisco, CA, USA, p. 170-178, 1997.
[22] D, Aleksovki; D, Kocev; S. Dzeroski. Evaluation of distance measures for hierarchical multilabel classification in functional genomics. In Proc. of the 1st Workshop on Learning from Multi-Label Data (MLD), p. 5¬16, 2009.
[23] F. Otero; A. Freitas; C. Johnson. A hierarchical classification ant colony algorithm for predicting gene ontology terms. In European Conference on Evolutionary Computation, achine Learning and Data Mining in Bioinformatics, volume LNCS, p. 68-79. Springer, 2009.
[24] H. B. Borges; J, C. Nievola. Hierarchical Classification using a Competitive Neural Network. In: 8th International Conference on Natural Computation (ICNC'12), 2012, Chongqing, China. 8th International Conference on Natural Computation (ICNC'12). Piscataway, NJ : IEEE Press, 2012. v. 1. p. 1-6.
[25] H. B. Borges; J, C. Nievola. Hierarchical Classification Using a Competitive Neural Network for Protein Function Prediction. In: 14th International Conference on Artificial Intelligence (ICAI'12), 2012, Las Vegas. 14th International Conference on Artificial Intelligence (ICAI'12). USA : CSREA Press, 2012. v. 1. p. 1-7.
[26] H. B. Borges; J, C. Nievola. Multi-Label Hierarchical Classification using a Competitive Neural Network for Protein Function Prediction. In: 2012 International Joint Conference on Neural Networks (IJCNN 2012), 2012, Brisbane, Austrália. 2012 International Joint Conference on Neural Networks (IJCNN 2012). Piscataway, NJ : IEEE Press, 2012. v. 1. p. 1-8.
[27] J. Desmar. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Resarch, 7:1-30, 2006.
[28] J. J. Burred; A. Lerch. A hierarchical approach to automatic musical genre classification. In Proc. of the 6th Int. Conf. on Digital Audio Effects, pp. 8-11, 2003.
[29] C. Decoro; Z. Barutcuoglu; R. Fiebrink. Bayesiana aggregation for hierarchical gene classification. In: Proc. of the 8th Int. Conf. on Music Information Retrieval, pp. 77-80, 2007.
[30] I. Dimitrovski; D. Kocev; S. Loskovska; S. Dzeroski. Hierarchical annotation of medical images. In Proc. of the 11th Int. Multiconference Information Society, A:174-177, 2008.
[31] F. Otero; A. Freitas; C. Johnson. A hierarchical multi-label classification ant colony algorithm for protein function prediction, Memetic Computing, vol. 2, pp. 165–181, 2010.
[32] L. Schietgat, C. Vens, J. Struyf, H. Blockeel, D. Kocev, and S. Dzeroski, "Predicting gene function using hierarchical multi-label decision tree ensembles", presented at BMC Bioinformatics, 2010.