Institute of Electrical and Biomedical Engineering
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- A new data mining approach for profiling and categorizing kinetic patterns of metabolic biomarkers after myocardial injury
positive and negative controls to assess generalizability of markers identified in PMI.
- Improving Phosphopeptide/Protein Identification Using a New Mining Framework for MS/MS Spectra Preprocessing
each peak representing a b or y ion. Next, low-probability peaks are removed from spectra, while remaining peaks have their intensities enhanced. As a result, a huge increase in signal-to-noise ratio is provided and the chances of detecting important peaks are significantly advanced. Experiments using MASCOT and SEQUEST along with Peptide/ProteinProphet and a decoy database approach showed a significant improvement in the sensitivity of phosphopeptide identification without compromising specificity, demonstrating that our new strategy for MS/MS spectra preprocessing is a powerful proteomics tool for enhancing phosphopeptide identifications.
- A new ensemble-based algorithm for identifying breath gas marker candidates in liver disease using ion molecule reaction mass spectrometry (IMR-MS)
Ion molecule reaction mass spectrometry (IMR-MS) was applied to a total of 126 human breath gas samples comprising 91 cases (AFLD, NAFLD and cirrhosis) and 35 healthy controls. A new feature selection modality termed Stacked Feature Ranking (SFR) was developed to identify potential liver disease marker candidates in breath gas samples, relying on the combination of different entropy-, correlation- and t-test- based feature ranking methods using a two-level architecture with a suggestion and a decision layer. We benchmarked SFR against four single feature selection methods, a wrapper and a recently described ensemble method, indicating a significantly higher discriminatory ability of up to 10-15% for the SFR selected gas compounds expressed by the area under the ROC curve of AUC=0.85-0.95. Using this approach, we were able to identify unexpected breath gas marker candidates in liver disease of high predictive value. A literature study further supports top ranked markers to be associated with liver disease. We propose SFR as a powerful tool for biomarker search in breath gas and other biological samples using mass spectrometry.
Netzer et al., Bioinformatics, 2009;25(7):941-947.
- A new rule-based algorithm for identifying metabolic markers in prostate cancer using tandem mass spectrometry
- SeMoP: A New Computational Strategy for the Unrestricted Search for Modified Peptides Using LC-MS/MS Data
Baumgartner et al., J Proteome Res, 2008;7(9):4199-208.
- LCF: Instance based classification with local density