title: Novel two-stage hybrid neural discriminant model for predicting proteins structural classes.
abstract: In order to establish novel hybrid neural discriminant model, linear discriminant analysis (LDA) was used at the first stage to evaluate the contribution of sequence parameters in determining the protein structural class. An in-house program generated parameters including single amino acid and all dipeptide composition frequencies for 498 proteins came from Zhou [An intriguing controversy over protein structural class prediction, J. Protein Chem. 17(8) (1998) 729-738]. Then, 127 statistically effective parameters were selected by stepwise LDA and were used as inputs of the artificial neural networks (ANNs) to build a two-stage hybrid predictor. In this study, self-consistency and jackknife tests were used to verify the performance of this hybrid model, and were compared with some of prior works. The results showed that our two-stage hybrid neural discriminant model approach is very promising and may play a complementary role to the existing powerful approaches.
authors.affiliation: Department of Biophysics, Faculty of Science, Tarbiat Modares University, Tehran, Iran.
authors.forename: Samad
authors.initials: S
authors.lastname: Jahandideh
authors.forename: Parviz
authors.initials: P
authors.lastname: Abdolmaleki
authors.forename: Mina
authors.initials: M
authors.lastname: Jahandideh
authors.forename: Ebrahim Barzegari
authors.initials: EB
authors.lastname: Asadabadi
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D055598:Chemical Phenomena; D002627:Chemistry, Physical; D003198:Computer Simulation; D030562:Databases, Protein; D016002:Discriminant Analysis; D016014:Linear Models; D008958:Models, Molecular; D016571:Neural Networks, Computer; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins