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We apply active learning and logistic regression to perform statistical analysis of Mascot peptide identification.Uncertainty sampling is used to select examples for labeling, and selected examples are labeled with reference data as the oracle. In each iteration of active learning, the penalized Newton-Raphson method is used to solve the logistic regression model. By testing the method on two datasets...
A single mass spectrometry experiment could produce hundreds of thousands of tandem mass spectra. Several search engines have been developed to interpret tandem mass spectra. All search engines need to determine the masses of peptide ions from their mass/charge ratios. Unfortunately, mass spectrometers do not detect the charges of ions. A current strategy is to search candidate peptides multiple times,...
Gene profile classification is achieved by casting the classification problem as finding the sparse representation of testing samples with respect to training samples. The sparse representation is found by subspace pursuit, which is much more efficient than linear programming techniques. The new approach, with no need of model selection, however, still has the performance which can match the best...
In the literature, hundreds of features have been proposed to assess the quality of tandem mass spectra. However, some features may be nearly irrelevant, and thus the inclusion of these nearly irrelevant features may degenerate the performance of quality assessment. This paper introduces a two-stage support vector machine recursive feature elimination (SVM-RFE) method to select the most relevant features...
A single mass spectrometry experiment could produce hundreds of thousands of tandem mass spectra. Several search engines have been developed to interpret tandem mass spectra. All search engines need to determine the masses of peptide ions from mass/charge ratios of ions. Unfortunately, mass spectrometers do not detect the charges of ions. A current strategy is to search candidate peptides multiply...
DNA microarray technologies allow for the simultaneous monitoring of thousands of genes, which reveal important information about cellular and tissue expression phenotypes. From a viewpoint of data analysis, microarray experiments may be classified into (1) classification of patients or non-patients or more subtypes in terms of gene expressions, (2) discovery of gene expression patterns over a set...
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