Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural networks and logistic regression: Part I
Computational Statistics & Data Analysis
Protein Architecture: A Practical Approach
Protein Architecture: A Practical Approach
Protein Fold Class Prediction: New Methods of Statistical Classification
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Neural Networks - Special issue on neural networks and kernel methods for structured domains
Computational Biology and Chemistry
Mathematical and Computer Modelling: An International Journal
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Statistical methods of discrimination and classification are used for the prediction of protein structure from amino acid sequence data. This provides information for the establishment of new paradigms of carcinogenesis modeling on the basis of gene expression. Feed forward neural networks and standard statistical classification procedures are used to classify proteins into fold classes. Logistic regression, additive models, and projection pursuit regression from the family of methods based on a posterior probabilities; linear, quadratic, and a flexible discriminant analysis from the class of methods based on class conditional probabilities, and the nearest-neighbors classification rule are applied to a data set of 268 sequences. From analyzing the prediction error obtained with a test sample (n = 125) and with a cross validation procedure, we conclude that the standard linear discriminant analysis and nearest-neighbor methods are at the same time statistically feasible and potent competitors to the more flexible tools of feed forward neural networks. Further research is needed to explore the gain obtainable from statistical methods by the application to larger sets of protein sequence data and to compare the results with those from biophysical approaches.