Abstract
Proteins are the main building blocks of the cell, and perform almost all the functions related to cell activity. Despite the recent advances in Molecular Biology, the function of a large amount of proteins is still unknown. The use of algorithms able to induce classification models is a promising approach for the functional prediction of proteins, whose classes are usually organized hierarchically. Among the machine learning techniques that have been used in hierarchical classification problems, one may highlight the Decision Trees. This paper describes the main characteristics of hierarchical classification models for Bioinformatics problems and applies three hierarchical methods based on the use of Decision Trees to protein functional classification datasets.
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Costa, E.P., Lorena, A.C., Carvalho, A.C.P.L.F., Freitas, A.A., Holden, N. (2007). Comparing Several Approaches for Hierarchical Classification of Proteins with Decision Trees. In: Sagot, MF., Walter, M.E.M.T. (eds) Advances in Bioinformatics and Computational Biology. BSB 2007. Lecture Notes in Computer Science(), vol 4643. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73731-5_12
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DOI: https://doi.org/10.1007/978-3-540-73731-5_12
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