Patent ID: 7899625

Claim:
A computer-implemented classification method, comprising programming a computer to perform: preprocessing mass spectrometry data, said preprocessing comprising creating a training data set and test data sets, conducting robust feature selection from the mass spectrometry data, said robust feature selection comprising: peak extraction to identify peaks in a data spectrum and to extract peaks from background data in the data spectrum; filtering data peaks extracted by said peak extraction; and selecting a support set of data on which an accurate weighting pattern-based model can be constructed by using a combinatorial pattern recognition algorithm to extract a large collection of data patterns from the set of data and from perturbations with experimental noise of the set of data; generating predictions for the test data, comprising mass spectrometry data, sets using multiple data classifiers, said multiple data classifiers comprising artificial neural networks, support vector machines, weighted voting on data patterns, classification and regression tress, k-nearest neighbor classification, and logistic regression; constructing and validating a meta-classifier by combining and averaging individual predictions of said multiple data classifiers to generate a robust prediction of a phenotype: mapping said individual classifiers to generate intermediate classifiers; and linearly combining said intermediate classifiers to generate a meta-classifier, wherein said test data sets are used exclusively for validation of the meta-classifier.