title: A novel feature representation method based on Chou's pseudo amino acid composition for protein structural class prediction.
abstract: During last few decades accurate determination of protein structural class using a fast and suitable computational method has been a challenging problem in protein science. In this context a meaningful representation of a protein sample plays a key role in achieving higher prediction accuracy. In this paper based on the concept of Chou's pseudo amino acid composition (Chou, K.C., 2001. Proteins 43, 246-255), a new feature representation method is introduced which is composed of the amino acid composition information, the amphiphilic correlation factors and the spectral characteristics of the protein. Thus the sample of a protein is represented by a set of discrete components which incorporate both the sequence order and the length effect. On the basis of such a statistical framework a simple radial basis function network based classifier is introduced to predict protein structural class. A set of exhaustive simulation studies demonstrates high success rate of classification using the self-consistency and jackknife test on the benchmark datasets.
authors.affiliation: Department of Electronics and Communication Engineering, National Institute of Technology, Orissa, India. sitanshusekhar@gmail.com
authors.forename: Sitanshu Sekhar
authors.initials: SS
authors.lastname: Sahu
authors.forename: Ganapati
authors.initials: G
authors.lastname: Panda
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry* / Q000145:classification / Q000235:genetics; D030562:Databases, Protein; D011506:Proteins / Q000737:chemistry* / Q000145:classification / Q000235:genetics; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D000596:Amino Acids; D011506:Proteins