Protein Fold Recognition using a Structural Hidden Markov Model

Authors:
D. Bouchaffra;J. Tan
Affiliations:
Oakland University Rochester, MI;Oakland University Rochester, MI
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Year:
2006

Citing 0
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Protein fold recognition with a two-layer method based on SVM-SA, WP-NN and C4.5 TLM-SNC

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Abstract

Protein fold recognition has been the focus of computational biologists for many years. In order to map a protein primary structure to its correct 3D fold, we introduce in this paper a machine learning paradigm that we entitled "structural hidden Markov model" (SHMM). We show how the concept of SHMM can efficiently use the protein secondary structure during the fold recognition task. Experimental results showed that the SHMMoutperforms the SVM with a 6% improvement in the average accuracy. However, because in this application the two classifiers are not correlated, therefore their combination based on the highest rank criterion boosted the SHMM average accuracy with 10%.