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Various algorithms have been proposed to identify protein complexes from PPI networks, based on the assumption that protein complexes are densely connected subgraphs. In this study, we conclude that most known protein complexes do not exhibit dense structures in S. cerevisiae PPI network, but maintain starlike structures in the network. Moreover, vertices of protein complexes are not sparsely connected...
A new method is presented to estimate the accuracy of peptide identification with logistic regression (LR)based on Sequest scores. Each peptide is characterized with the regularized Sequest scores ?Cn* and Xcorr*. The score regularization is formulated as an optimization problem by applying two assumptions: the smoothing consistency between sibling peptides and the fitting consistency between original...
Protein complexes are essential entities that perform the major cellular processes and biological functions in live organisms. The identification of component proteins in a complex from protein-protein interaction (PPI) networks is an important step to understand the organization and interaction of gene products. In existing literature, methods for identifying protein complexes typically start from...
Charge states of tandem mass spectra from low-resolution collision induced dissociation can not be determined by mass spectrometry. As a result, such spectra with multiple charges are usually searched multiple times by assuming each possible charge state. Not only does this strategy increase the overall database search time, but also yields more false positives. In this paper, we propose a new approach...
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,...
Several computational methods have been proposed to assess the quality of tandem mass spectra. These methods range from supervised to unsupervised algorithms, discriminative to generative models. Unsupervised learning algorithms for tandem mass spectra are not probabilistic model based and they don't provide probabilities for spectra quality assessment. In this study, the distribution of high quality...
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...
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