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Optimally combining available information is one of the key challenges in knowledge-driven prediction techniques. In this study, we evaluate six Phi and Psi-based backbone alphabets. We show that the addition of predicted backbone conformations to SVM classifiers can improve fold recognition. Our experimental results show that the inclusion of predicted backbone conformations in our feature representation...
A novel method of feature extraction form protein sequences, structures and physicochemical properties has been proposed and obtained a better classification results by the key eigenvector obtained form knowledge reduction combined with the algorithm of support vector machine. Based on Jackknife detecting methods, the comprehensive classification results 78.3% and 90.9% for all-??, all-??, ??+?? and...
We study the single class SVM (SCSVM) classifier performance on the positive data points while considering the impact of SCSVM on negative protein pair data points. We compare the result with the AA classifier (amino acids maximum entropy classifier) [9] to see if a better performance can be achieved for the same data configuration. The conclusion is that although positive classifier is slightly better...
Many statistical measures and algorithmic techniques have been proposed for studying residue coupling in protein families. Generally speaking, two residue positions are considered coupled if, in the sequence record, some of their amino acid type combinations are significantly more common than others. While the proposed approaches have proven useful in finding and describing coupling, a significant...
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