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We first propose a new method to process peptide identification reports from databases search engines. Then via it we develop a method for unifying protein inference and peptide identification by adding a feedback from protein inference to peptide identification. The feedback information is a list of high‐confidence proteins, which is used to update an adjacency matrix between peptides. The adjacency...
The identification of protein complexes plays a key role in understanding major cellular processes and biological functions. Various computational algorithms have been proposed to identify protein complexes from protein–protein interaction (PPI) networks. In this paper, we first introduce a new seed‐selection strategy for seed‐growth style algorithms. Cliques rather than individual vertices are employed...
In our recent work on denoising, a linear combination of five features was used to adjust the peak intensities in tandem mass spectra. Although the method showed a promise, the coefficients (weights) of the linear combination were fixed and determined empirically. In this paper, we proposed an adaptive approach for estimating these weights. The proposed approach: (i) calculates the score for each...
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...
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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