Protein sequence motifs are gathering progressively attention in the sequence analysis area. The conserved regions have the potential to determine the conformation, function and activities of the proteins. We develop a new method combines the concept of granular computing and the power of Ranking-SVM to further extract protein sequence motif information generated from the FGK model. The quality of motif information increases dramatically in all three evaluation measures by applying this new feature elimination model. Since the training step of Ranking SVM is very time consuming, we provide a feasible way to reduce the training time dramatically without sacrificing the quality.
['Bernard Chen', 'Stephen Pellicer', 'Phang C. Tai', 'Robert W. Harrison', 'Yi Pan']
Efficient Super Granular SVM Feature Elimination (Super GSVM-FE) model for protein sequence motif information extraction