Method and system for robust classification strategy for cancer detection from mass spectrometry data
First Claim
1. A robust classification method for cancer detection from mass spectrometry data, comprising:
- preprocessing mass spectrometry data;
conducting robust feature selection from the mass spectrometry data;
generating predictions for test 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 trees, k-nearest neighbor classification, and logistic regression; and
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,wherein said test data sets are used exclusively for validation of the meta-classifier.
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Abstract
A robust classification method for cancer detection from mass spectrometry data includes inputting the mass spectrometry data, preprocessing the spectrometry data, conducting robust feature selection, generating predictions for the test data sets using multiple data classifiers, the multiple data classifiers including artificial neural networks, support vector machines, weighted voting on data patterns, classification and regression trees, k-nearest neighbor classification, and logistic regression, and constructing and validating a meta-classifier by combining individual predictions of the multiple data classifiers to generate a robust prediction of a phenotype. The test data sets are used exclusively for validation of the meta-classifier.
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Citations
20 Claims
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1. A robust classification method for cancer detection from mass spectrometry data, comprising:
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preprocessing mass spectrometry data; conducting robust feature selection from the mass spectrometry data; generating predictions for test 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 trees, k-nearest neighbor classification, and logistic regression; and 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, wherein said test data sets are used exclusively for validation of the meta-classifier. - View Dependent Claims (2)
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3. A classification method, comprising:
applying machine learning tools on a set of data to create a robust predictive model. - View Dependent Claims (4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20)
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18. A classification system, comprising:
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a feature selection unit that conducts robust feature selection of a set of data; a prediction unit that generates predictions for said set of data; and a validating unit that validates said predictions.
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Specification