Abstract
Protein structure prediction is an important area of research in bioinformatics. In this research, a novel method to predict the structure of the protein is introduced. The amino acid frequencies, generalization dipeptide composition and typical hydrophobic composition of protein structure are treated as candidate feature. Flexible neural tree and neural network are employed as classification model. To evaluate the efficiency of the proposed method, a classical protein sequence dataset (1189) is selected as the test dataset. The results show that the method is efficient for protein structure prediction.
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Bao, W., Chen, Y., Wang, D., kong, F., Yu, G. (2014). Prediction of Protein Structure Classes with Ensemble Classifiers. In: Huang, DS., Han, K., Gromiha, M. (eds) Intelligent Computing in Bioinformatics. ICIC 2014. Lecture Notes in Computer Science(), vol 8590. Springer, Cham. https://doi.org/10.1007/978-3-319-09330-7_40
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DOI: https://doi.org/10.1007/978-3-319-09330-7_40
Publisher Name: Springer, Cham
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