In this work, we propose a new computational technique to solve the protein classification problem. The goal is to predict the functional family of novel protein sequences based on their motif composition. In order to improve the results obtained with other known approaches, we propose a new data mining technique for protein classification based on Bayes' theorem, called Highest Subset Probability (HiSP). To evaluate our proposal, datasets extracted from Prosite, a curated protein family database, are used as experimental datasets. The computational results have shown that the proposed method outperforms other known methods for all tested datasets and looks very promising for problems with characteristics similar to the problem addressed here. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Merschmann, L., & Plastino, A. (2006). HiSP: A probabilistic data mining technique for protein classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3992 LNCS-II, pp. 863–870). Springer Verlag. https://doi.org/10.1007/11758525_115
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