title: Protein fold class prediction: new methods of statistical classification.
abstract: Feed forward neural networks are compared with standard and new statistical classification procedures for the classification of proteins. We applied logistic regression, an additive model and projection pursuit regression from the methods based on a posterior probabilities; linear, quadratic and a flexible discriminant analysis from the methods based on class conditional probabilities, and the K-nearest-neighbors classification rule. Both, the apparent error rate obtained with the training sample (n = 143) and the test error rate obtained with the test sample (n = 125) and the 10-fold cross validation error were calculated. We conclude that some of the standard statistical methods are potent competitors to the more flexible tools of machine learning.
authors.affiliation: Department of Statistics, Stanford University, Palo Alto, CA 94305-4065, USA.
authors.forename: J
authors.initials: J
authors.lastname: Grassmann
authors.forename: M
authors.initials: M
authors.lastname: Reczko
authors.forename: S
authors.initials: S
authors.lastname: Suhai
authors.forename: L
authors.initials: L
authors.lastname: Edler
mesh_terms: D000465:Algorithms; D016015:Logistic Models; D016571:Neural Networks, Computer; D011336:Probability; D017510:Protein Folding; D012984:Software