Abstract
This paper addresses the classification task of data mining (a form of supervised learning) in the context of an important bioinformatics problem, namely the prediction of protein functions. This problem is cast as a hierarchical classification problem, where the protein functions to be predicted correspond to classes that are arranged in a hierarchical structure, in the form of a class tree. The main contribution of this paper is to propose a new Artificial Immune System that creates a new representation for proteins, in order to maximize the predictive accuracy of a hierarchical classification algorithm applied to the corresponding protein function prediction problem.
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Secker, A., Davies, M.N., Freitas, A.A., Timmis, J., Clark, E., Flower, D.R. (2008). An Artificial Immune System for Evolving Amino Acid Clusters Tailored to Protein Function Prediction. In: Bentley, P.J., Lee, D., Jung, S. (eds) Artificial Immune Systems. ICARIS 2008. Lecture Notes in Computer Science, vol 5132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85072-4_22
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DOI: https://doi.org/10.1007/978-3-540-85072-4_22
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