Abstract
A novel hybrid genetic algorithm(GA)/Support Vector Machine (SVM) system, which selects features from the protein sequences and trains the SVM classifier simultaneously using a multi-objective genetic algorithm, is proposed in this paper. The system is then applied to classify protein sequences obtained from the Protein Information Resource (PIR) protein database. Finally, experimental results over six protein superfamilies are reported, where it is shown that the proposed hybrid GA/SVM system outperforms BLAST and HMMer. © Springer-Verlag Berlin Heidelberg 2004.
Cite
CITATION STYLE
Zhao, X. M., Huang, D. S., Cheung, Y. M., Wang, H. Q., & Huang, X. (2004). A novel hybrid GA/SVM system for protein sequences classification. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3177, 11–16. https://doi.org/10.1007/978-3-540-28651-6_2
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