title: Novel efficient granular computing models for protein sequence motifs and structure information discovery.
abstract: Protein sequence motifs have the potential to determine the conformation, function and activities of the proteins. In order to obtain protein sequence motifs which are universally conserved across protein family boundaries, unlike most popular motif discovering algorithms, our input dataset is extremely large. As a result, an efficient technique is demanded. We create two granular computing models to efficiently generate protein motif information which transcend protein family boundaries. We have performed a comprehensive comparison between the two models. In addition, we further combine the results from the FIK and FGK models to generate our best sequence motif information.
authors.affiliation: Department of Computer Science, University of Central Arkansas, 201 Donaghey Ave. MCST304, Conway, AR 72035, USA. bchen@uca.edu
authors.forename: Bernard
authors.initials: B
authors.lastname: Chen
authors.forename: Stephen
authors.initials: S
authors.lastname: Pellicer
authors.forename: Phang C
authors.initials: PC
authors.lastname: Tai
authors.forename: Robert
authors.initials: R
authors.lastname: Harrison
authors.forename: Yi
authors.initials: Y
authors.lastname: Pan
mesh_terms: D000465:Algorithms; D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods*; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins