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Title: Protein structure and fold prediction using Tree-Augmented naïve Bayesian classifier
Authors: Chinnasamy, A.
Sung, W.-K. 
Mittal, A.
Keywords: Bayesian classifier
Protein feature extraction
Protein fold class prediction
Issue Date: 2005
Citation: Chinnasamy, A.,Sung, W.-K.,Mittal, A. (2005). Protein structure and fold prediction using Tree-Augmented naïve Bayesian classifier. Journal of Bioinformatics and Computational Biology 3 (4) : 803-819. ScholarBank@NUS Repository.
Abstract: Due to the large volume of protein sequence data, computational methods to determine the structure class and the fold class of a protein sequence have become essential. Several techniques based on sequence similarity, Neural Networks, Support Vector Machines (SVMs), etc. have been applied. Since most of these classifiers use binary classifiers for multi-classification, there may be Nc2 classifiers required. This paper presents a framework using the Tree-Augmented Bayesian Networks (TAN) which performs multi-classification based on the theory of learning Bayesian Networks and using improved feature vector representation of (Ding et al., 2001). In order to enhance TAN's performance, pre-processing of data is done by feature discretization and post-processing is done by using Mean Probability Voting (MPV) scheme. The advantage of using Bayesian approach over other learning methods is that the network structure is intuitive. In addition, one can read off the TAN structure probabilities to determine the significance of each feature (say, hydrophobicity) for each class, which helps to further understand the complexity in protein structure. The experiments on the datasets used in three prominent recent works show that our approach is more accurate than other discriminative methods. The framework is implemented on the BAYESPROT web server and it is available at Default.htm. More detailed results are also available on the above website. © Imperial College Press.
Source Title: Journal of Bioinformatics and Computational Biology
ISSN: 02197200
DOI: 10.1142/S0219720005001302
Appears in Collections:Staff Publications

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