{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T21:21:20Z","timestamp":1696022480183},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"14","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2010,7,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: The discovery of new and unexpected biomarkers in cardiovascular disease is a highly data-driven process that requires the complementary power of modern metabolite profiling technologies, bioinformatics and biostatistics. Clinical biomarkers of early myocardial injury are lacking. A prospective biomarker cohort study was carried out to identify, categorize and profile kinetic patterns of early metabolic biomarkers of planned myocardial infarction (PMI) and spontaneous (SMI) myocardial infarction. We applied a targeted mass spectrometry (MS)-based metabolite profiling platform to serial blood samples drawn from carefully phenotyped patients undergoing alcohol septal ablation for hypertrophic obstructive cardiomyopathy serving as a human model of PMI. Patients with SMI and patients undergoing catheterization without induction of myocardial infarction served as positive and negative controls to assess generalizability of markers identified in PMI.<\/jats:p>\n               <jats:p>Results: To identify metabolites of high predictive value in tandem mass spectrometry data, we introduced a new feature selection method for the categorization of metabolic signatures into three classes of weak, moderate and strong predictors, which can be easily applied to both paired and unpaired samples. Our paradigm outperformed standard null-hypothesis significance testing and other popular methods for feature selection in terms of the area under the receiver operating curve and the product of sensitivity and specificity. Our results emphasize that this new method was able to identify, classify and validate alterations of levels in multiple metabolites participating in pathways associated with myocardial injury as early as 10 min after PMI.<\/jats:p>\n               <jats:p>Availability: The algorithm as well as supplementary material is available for download at: www.umit.at\/page.cfm?vpath=departments\/technik\/iebe\/tools\/bi<\/jats:p>\n               <jats:p>Contact: \u00a0christian.baumgartner@umit.at<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btq254","type":"journal-article","created":{"date-parts":[[2010,5,19]],"date-time":"2010-05-19T00:47:01Z","timestamp":1274230021000},"page":"1745-1751","source":"Crossref","is-referenced-by-count":30,"title":["A new data mining approach for profiling and categorizing kinetic patterns of metabolic biomarkers after myocardial injury"],"prefix":"10.1093","volume":"26","author":[{"given":"Christian","family":"Baumgartner","sequence":"first","affiliation":[{"name":"1 Research Group for Clinical Bioinformatics, Institute of Electrical, Electronic and Bioengineering, University for Health Sciences, Medical Informatics and Technology (UMIT), A-6060 Hall in Tirol, Austria, 2 Cardiology Division, 3 Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, 4 Donald W. Reynolds Cardiovascular Clinical Research Center on Atherosclerosis at Harvard Medical School, Boston and 5 Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"}]},{"given":"Gregory D.","family":"Lewis","sequence":"additional","affiliation":[{"name":"1 Research Group for Clinical Bioinformatics, Institute of Electrical, Electronic and Bioengineering, University for Health Sciences, Medical Informatics and Technology (UMIT), A-6060 Hall in Tirol, Austria, 2 Cardiology Division, 3 Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, 4 Donald W. Reynolds Cardiovascular Clinical Research Center on Atherosclerosis at Harvard Medical School, Boston and 5 Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"name":"1 Research Group for Clinical Bioinformatics, Institute of Electrical, Electronic and Bioengineering, University for Health Sciences, Medical Informatics and Technology (UMIT), A-6060 Hall in Tirol, Austria, 2 Cardiology Division, 3 Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, 4 Donald W. Reynolds Cardiovascular Clinical Research Center on Atherosclerosis at Harvard Medical School, Boston and 5 Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"name":"1 Research Group for Clinical Bioinformatics, Institute of Electrical, Electronic and Bioengineering, University for Health Sciences, Medical Informatics and Technology (UMIT), A-6060 Hall in Tirol, Austria, 2 Cardiology Division, 3 Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, 4 Donald W. Reynolds Cardiovascular Clinical Research Center on Atherosclerosis at Harvard Medical School, Boston and 5 Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"}]},{"given":"Michael","family":"Netzer","sequence":"additional","affiliation":[{"name":"1 Research Group for Clinical Bioinformatics, Institute of Electrical, Electronic and Bioengineering, University for Health Sciences, Medical Informatics and Technology (UMIT), A-6060 Hall in Tirol, Austria, 2 Cardiology Division, 3 Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, 4 Donald W. Reynolds Cardiovascular Clinical Research Center on Atherosclerosis at Harvard Medical School, Boston and 5 Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"}]},{"given":"Bernhard","family":"Pfeifer","sequence":"additional","affiliation":[{"name":"1 Research Group for Clinical Bioinformatics, Institute of Electrical, Electronic and Bioengineering, University for Health Sciences, Medical Informatics and Technology (UMIT), A-6060 Hall in Tirol, Austria, 2 Cardiology Division, 3 Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, 4 Donald W. Reynolds Cardiovascular Clinical Research Center on Atherosclerosis at Harvard Medical School, Boston and 5 Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"}]},{"given":"Robert E.","family":"Gerszten","sequence":"additional","affiliation":[{"name":"1 Research Group for Clinical Bioinformatics, Institute of Electrical, Electronic and Bioengineering, University for Health Sciences, Medical Informatics and Technology (UMIT), A-6060 Hall in Tirol, Austria, 2 Cardiology Division, 3 Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, 4 Donald W. Reynolds Cardiovascular Clinical Research Center on Atherosclerosis at Harvard Medical School, Boston and 5 Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"name":"1 Research Group for Clinical Bioinformatics, Institute of Electrical, Electronic and Bioengineering, University for Health Sciences, Medical Informatics and Technology (UMIT), A-6060 Hall in Tirol, Austria, 2 Cardiology Division, 3 Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, 4 Donald W. Reynolds Cardiovascular Clinical Research Center on Atherosclerosis at Harvard Medical School, Boston and 5 Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"name":"1 Research Group for Clinical Bioinformatics, Institute of Electrical, Electronic and Bioengineering, University for Health Sciences, Medical Informatics and Technology (UMIT), A-6060 Hall in Tirol, Austria, 2 Cardiology Division, 3 Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, 4 Donald W. Reynolds Cardiovascular Clinical Research Center on Atherosclerosis at Harvard Medical School, Boston and 5 Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"},{"name":"1 Research Group for Clinical Bioinformatics, Institute of Electrical, Electronic and Bioengineering, University for Health Sciences, Medical Informatics and Technology (UMIT), A-6060 Hall in Tirol, Austria, 2 Cardiology Division, 3 Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, 4 Donald W. Reynolds Cardiovascular Clinical Research Center on Atherosclerosis at Harvard Medical School, Boston and 5 Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA"}]}],"member":"286","published-online":{"date-parts":[[2010,5,18]]},"reference":[{"key":"2023012507593598800_B1","doi-asserted-by":"crossref","first-page":"525","DOI":"10.2174\/138920006777697918","article-title":"The role of mass spectrometry in biomarker discovery and measurement","volume":"7","author":"Ackermann","year":"2006","journal-title":"Curr. Drug Metab."},{"key":"2023012507593598800_B2","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1177\/1087057105280518","article-title":"Biomarker discovery, disease classification, and similarity query processing on high-throughput MS\/MS data of inborn errors of metabolism","volume":"11","author":"Baumgartner","year":"2006","journal-title":"J. Biomol. 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