Abstract
This work proposes two versions of an Artificial Immune System (AIS) - a relatively recent computational intelligence paradigm – for predicting protein functions described in the Gene Ontology (GO). The GO has functional classes (GO terms) specified in the form of a directed acyclic graph, which leads to a very challenging multi-label hierarchical classification problem where a protein can be assigned multiple classes (functions, GO terms) across several levels of the GO’s term hierarchy. Hence, the proposed approach, called MHC-AIS (Multi-label Hierarchical Classification with an Artificial Immune System), is a sophisticated classification algorithm tailored to both multi-label and hierarchical classification. The first version of the MHC-AIS builds a global classifier to predict all classes in the application domain, whilst the second version builds a local classifier to predict each class. In both versions of the MHC-AIS the classifier is expressed as a set of IF-THEN classification rules, which have the advantage of representing comprehensible knowledge to biologist users. The two MHC-AIS versions are evaluated on a dataset of DNA-binding and ATPase proteins.
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References
De Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, Berlin (2002)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Mateo (2005)
Fogel, G.B., Corne, D.W.: Evolutionary Computation in Bioinformatics. Morgan Kaufmann Publishers, San Franciso (2003)
The Gene Ontology Consortium. The Gene Ontology (GO) Database and Informatics Resource. Nucleic Acids Research 32(1), 258–261 (2004)
Freitas, A.A.: Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer, Berlin (2002)
Tsoumakas, G., Katakis, I.: Multi-Label Classification: An Overview. International Journal of Data Warehousing and Mining 3(3), 1–13 (2007)
Sun, A., Lim, E.-P., Ng, W.-K.: Performance Measurement Framework for Hierarchical Text Classification. Journal of the American Society for Information Science and Technology 54(11), 1014–1028 (2003)
E. Nomenclature, of the IUPAC-IUB. American Elsevier Pub. Co., New York, NY 104 (1972)
Freitas, A.A., Timmis, T.: Revisiting the foundations of artificial immune systems for data mining. IEEE Trans. on Evolutionary Computation 11(4), 521–540 (2007)
Ada, G.L., Nossal, G.V.: The Clonal Selection Theory. Scientific American 257, 50–57 (1987)
Jerne, N.K.: Towards a Network Theory of Immune System. Ann. Immunol (Inst. Pasteur) 125C, 373–389 (1974)
Alves, R.T., Delgado, M.R., Lopes, H.S., Freitas, A.A.: An artificial immune system for fuzzy-rule induction in data mining. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 1011–1020. Springer, Heidelberg (2004)
Goldberg, D.E.: Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley, Reading (1989)
The UniProt Consortium. The Universal Protein Resource (UniProt). Nucleic Acids Res. 35, D193–D197 (2007)
Alberts, B., Johnson, A., Lewis, J., Raff, M., Roberts, K., Water, P.: Molecular Biology of the Cell, 4th edn. Garland Science, New York (2002)
Hulo, N., Bairoch, A., Bulliard, V., Cerutti, L., De Castro, E., Langendijk-Genevaux, P.S., Pagni, M., Sigrist, C.J.A.: The PROSITE Database. Nucleic Acids Res. 34, D227–D230 (2006)
Wolstencroft, K., Lord, P.W., Tabernero, P., Brass, P., Stevens, R.: Protein classification using ontology classification. Bioinformatics 22, 530–538 (2006)
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Alves, R.T., Delgado, M.R., Freitas, A.A. (2008). Multi-label Hierarchical Classification of Protein Functions with Artificial Immune Systems. In: Bazzan, A.L.C., Craven, M., Martins, N.F. (eds) Advances in Bioinformatics and Computational Biology. BSB 2008. Lecture Notes in Computer Science(), vol 5167. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85557-6_1
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DOI: https://doi.org/10.1007/978-3-540-85557-6_1
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