Artificial Neural Network Analysis for Evaluation of Peptide MS/MS Spectra in Proteomics

@article{citeulike:1679944, abstract = {Abstract: The aim of the work was to explore usefulness of artificial neural network (ANN) analysis for the evaluation of proteomics data. The analysis was applied to the data generated by the widely used protein identification program Sequest, completed with several structural parameters readily calculated from peptide molecular formulas. Proteins from yeast cells were identified based on the MS/MS spectra of peptides. The constructed ANN was demonstrated to classify automatically as either "good" or "bad" the peptide MS/MS spectra otherwise classified manually. An appropriately trained ANN proves to be a high-throughput tool facilitating examination of Sequest's results. ANNs are recommended as a means of automatic processing of large amounts of MS/MS data, which normally must be considered in the analysis of complex mixtures of proteins in proteomics.}, address = {Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdask, Gen. J. Hallera 107, 80-416 Gdask, Poland, Division of Food Science, Institute of Animal Reproduction and Food Research of the Polish Academy of Sciences, Tuwima 10, 10-747 Olsztyn, Poland, and Harvard NIEHS Center for Environmental Health Proteomics Facility, Harvard School of Public Health, 665 Huntington Avenue, Boston, Massachusetts 02115}, author = {Baczek, T. and Bucinski, A. and Ivanov, A. R. and Kaliszan, R. }, citeulike-article-id = {1679944}, doi = {10.1021/ac030297u}, journal = {Anal. Chem.}, keywords = {machine-learning, peptide-identification, peptide-identification-statistics, proteomics, tandem-mass-spectrometry}, month = {March}, number = {6}, pages = {1726--1732}, posted-at = {2007-09-20 18:12:53}, priority = {2}, title = {Artificial Neural Network Analysis for Evaluation of Peptide MS/MS Spectra in Proteomics}, url = {http://dx.doi.org/10.1021/ac030297u}, volume = {76}, year = {2004} }

TY - JOUR ID - citeulike:1679944 L3 - citeulike-article-id:1679944 TI - Artificial Neural Network Analysis for Evaluation of Peptide MS/MS Spectra in Proteomics JF - Anal. Chem. VL - 76 IS - 6 SP - 1726 EP - 1732 AD - Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdask, Gen. J. Hallera 107, 80-416 Gdask, Poland, Division of Food Science, Institute of Animal Reproduction and Food Research of the Polish Academy of Sciences, Tuwima 10, 10-747 Olsztyn, Poland, and Harvard NIEHS Center for Environmental Health Proteomics Facility, Harvard School of Public Health, 665 Huntington Avenue, Boston, Massachusetts 02115 N2 - Abstract: The aim of the work was to explore usefulness of artificial neural network (ANN) analysis for the evaluation of proteomics data. The analysis was applied to the data generated by the widely used protein identification program Sequest, completed with several structural parameters readily calculated from peptide molecular formulas. Proteins from yeast cells were identified based on the MS/MS spectra of peptides. The constructed ANN was demonstrated to classify automatically as either "good" or "bad" the peptide MS/MS spectra otherwise classified manually. An appropriately trained ANN proves to be a high-throughput tool facilitating examination of Sequest's results. ANNs are recommended as a means of automatic processing of large amounts of MS/MS data, which normally must be considered in the analysis of complex mixtures of proteins in proteomics. KW - machine-learning KW - peptide-identification KW - peptide-identification-statistics KW - proteomics KW - tandem-mass-spectrometry AU - Baczek, T AU - Bucinski, A AU - Ivanov, AR AU - Kaliszan, R PY - 2004/03/15/ UR - http://dx.doi.org/10.1021/ac030297u ER -