A lazy data mining approach for protein classification.
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. In addition, our experiments suggest that HiSP performs well on highly imbalanced datasets.
Merschmann
Luiz
L
Department of Computer Science, Fluminense Federal University, Niterói, Brazil. lmerschmann@ic.uff.br
Plastino
Alexandre
A
D000465:Algorithms; D000595:Amino Acid Sequence; D003628:Database Management Systems; D030562:Databases, Protein; D016247:Information Storage and Retrieval / Q000379:methods*; D008969:Molecular Sequence Data; D011506:Proteins / Q000737:chemistry* / Q000145:classification; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid
D011506:Proteins