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Proteins can be classified among the four structural classes of All-Alpha, All-Beta, Alpha+Beta and Alpha/Beta which are further subdivided into 27 folds. Protein fold classification problem is cited in the literature as a challenging unbalanced classification problem with the accuracy results being as low as 51.1% on the bench mark data set of Ding et al. and highest accuracy at 60.5% using 2500...
Automatic prediction of protein three dimensional structures from its amino acid sequence has become one of the most important and researched fields in bioinformatics We attempt to solve this problem using machine learning technique and information from both sequence and structure of the protein. Information like amino acid substitution matrix, polarity, secondary structure information and relative...
Understanding protein structures is vital to determining the function of a protein and its interaction with DNA, RNA and enzyme. The information about its conformation can provide essential information for drug design and protein engineering. While there are over a million known protein sequences, only a limited number of protein structures are experimentally determined. Hence, prediction of protein...
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...
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