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Recent work using supervised learning for protein structure prediction has achieved state-of-the-art classification performance. However, such methods are based only on labeled data, while in practice the labeled data is so few and expensive to obtain and unlabeled data is far more plentiful. An effective way to enhance the performance of the learned hypothesis by using the labeled and unlabeled data...
Protein sequence motifs are gathering more and more attention in the sequence analysis area. These recurring regions have the potential to determine protein 's conformation, function and activities. In our previous work, we tried to obtain protein sequence motifs which are universally conserved across protein family boundaries. Therefore, unlike most popular motif discovering algorithms, our input...
Protein sequence motifs are gathering more and more attention in the sequence analysis area. These recurring regions have the potential to determine protein's conformation, function and activities. In our previous work, we tried to obtain protein sequence motifs which are universally conserved across protein family boundaries. Therefore, unlike most popular motif discovering algorithms, our input...
SecA is an important component of protein translocation in bacteria, and exists in soluble and membrane-integrated forms. Most membrane prediction programs predict SecA as being a soluble protein, with the exception of TMpred and TopPred. However, the membrane associated predicted segments by TMpred and TopPred are inconsistent across bacterial species in spite of high sequence homology. In this paper...
With the efforts to understand protein structure, many computational approaches have been made recently. Among them, the support vector machine (SVM) methods have been recently applied and showed successful performance compared with other machine learning schemes. However, despite the high performance, the SVM approaches suffer from the problem of understandability since it is a black-box model. To...
Protein sequence motifs information is very important to the analysis of biologically significant regions. The conserved regions have the potential to determine the conformation, function and activities of the proteins. The main purpose of this paper is trying to obtain protein sequence motifs which are universally conserved and across protein family boundaries. Therefore, unlike most popular motif...
The PHI-BLAST algorithm for protein sequence alignment takes a query sequence and searches a protein database for a small seed or region of high similarity and extends this alignment to produce the total alignment for sequences. Clearly, the success of this approach depends on the quality of the seeds. We propose an algorithm that maximizes the likelihood of seeds sharing the same local structure...
Protein secondary structure prediction has a fundamental influence on today's bioinformatics research. In this work, binary and tertiary classifiers of protein secondary structure prediction are implemented on Denoeux belief neural network (DBNN) architecture. Hydrophobicity matrix, orthogonal matrix, BLOSUM62 and PSSM (position specific scoring matrix) are experimented separately as the encoding...
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