title: Analysis and identification of beta-turn types using multinomial logistic regression and artificial neural network.
abstract: BACKGROUND
  So far various statistical and machine learning techniques applied for prediction of beta-turns. The majority of these techniques have been only focused on the prediction of beta-turn location in proteins. We developed a hybrid approach for analysis and prediction of different types of beta-turn.
  
  
  RESULTS
  A two-stage hybrid model developed to predict the beta-turn Types I, II, IV and VIII. Multinomial logistic regression was initially used for the first time to select significant parameters in prediction of beta-turn types using a self-consistency test procedure. The extracted parameters were consisted of 80 amino acid positional occurrences and 20 amino acid percentages in beta-turn sequence. The most significant parameters were then selected using multinomial logistic regression model. Among these, the occurrences of glutamine, histidine, glutamic acid and arginine, respectively, in positions i, i + 1, i + 2 and i + 3 of beta-turn sequence had an overall relationship with five beta-turn types. A neural network model was then constructed and fed by the parameters selected by multinomial logistic regression to build a hybrid predictor. The networks have been trained and tested on a non-homologous dataset of 565 protein chains by 9-fold cross-validation. It has been observed that the hybrid model gives a Matthews correlation coefficient (MCC) of 0.235, 0.473, 0.103 and 0.124, respectively, for beta-turn Types I, II, IV and VIII. Our model also distinguished the different types of beta-turn in the embedded binary logit comparisons which have not carried out so far.
  
  
  BACKGROUND
  Available on request from the authors.
authors.affiliation: Department of Biophysics, Faculty of Basic Sciences, Tarbiat Modares University, Tehran, Iran.
authors.forename: Mehdi Poursheikhali
authors.initials: MP
authors.lastname: Asgary
authors.forename: Samad
authors.initials: S
authors.lastname: Jahandideh
authors.forename: Parviz
authors.initials: P
authors.lastname: Abdolmaleki
authors.forename: Anoshirvan
authors.initials: A
authors.lastname: Kazemnejad
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D003198:Computer Simulation; D016015:Logistic Models; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D011487:Protein Conformation; D012044:Regression Analysis; D020539:Sequence Analysis, Protein / Q000379:methods*