{"id":"https://openalex.org/W2119942013","doi":"https://doi.org/10.1093/bib/bbn009","title":"Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods","display_name":"Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods","publication_year":2007,"publication_date":"2007-09-28","ids":{"openalex":"https://openalex.org/W2119942013","doi":"https://doi.org/10.1093/bib/bbn009","mag":"2119942013","pmid":"https://pubmed.ncbi.nlm.nih.gov/18334515"},"language":"en","primary_location":{"id":"doi:10.1093/bib/bbn009","is_oa":true,"landing_page_url":"https://doi.org/10.1093/bib/bbn009","pdf_url":"https://academic.oup.com/bib/article-pdf/9/2/129/774623/bbn009.pdf","source":{"id":"https://openalex.org/S91767247","display_name":"Briefings in Bioinformatics","issn_l":"1467-5463","issn":["1467-5463","1477-4054"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Briefings in Bioinformatics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://academic.oup.com/bib/article-pdf/9/2/129/774623/bbn009.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108622305","display_name":"T. Villmann","orcid":null},"institutions":[{"id":"https://openalex.org/I926574661","display_name":"Leipzig University","ror":"https://ror.org/03s7gtk40","country_code":"DE","type":"education","lineage":["https://openalex.org/I926574661"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"T. Villmann","raw_affiliation_strings":["Medical Department, University Leipzig Germany. thomas.villmann@medizin.uni-leipzig.de"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Medical Department, University Leipzig Germany. thomas.villmann@medizin.uni-leipzig.de","institution_ids":["https://openalex.org/I926574661"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004196006","display_name":"Frank-Michael Schleif","orcid":"https://orcid.org/0000-0002-7539-1283"},"institutions":[{"id":"https://openalex.org/I16718484","display_name":"Hess (United States)","ror":"https://ror.org/00zbk1w77","country_code":"US","type":"company","lineage":["https://openalex.org/I16718484"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"F.-M. Schleif","raw_affiliation_strings":["Frank-Michael Schleif studied Computer Science at Leipzig University, graduating in 2002. He then became a PhD student at Leipzig University. In 2003, he joined Bruker Biosciences and continued his PhD studies at the Clausthal University of Technology. In 2006, he received a PhD in Computer Science and was awarded as the best C.S. PhD thesis at TUC. He is currently a research scientist in the MetaSTEM project team at IZKF (Interdisciplinary Center for Clinical Research) and a member of the Computational Intelligence Group at the Medical Department of the Leipzig University. His research activities focus on machine learning methods, statistical data analysis and algorithm development"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Frank-Michael Schleif studied Computer Science at Leipzig University, graduating in 2002. He then became a PhD student at Leipzig University. In 2003, he joined Bruker Biosciences and continued his PhD studies at the Clausthal University of Technology. In 2006, he received a PhD in Computer Science and was awarded as the best C.S. PhD thesis at TUC. He is currently a research scientist in the MetaSTEM project team at IZKF (Interdisciplinary Center for Clinical Research) and a member of the Computational Intelligence Group at the Medical Department of the Leipzig University. His research activities focus on machine learning methods, statistical data analysis and algorithm development","institution_ids":["https://openalex.org/I16718484"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102979137","display_name":"Markus Kostrzewa","orcid":"https://orcid.org/0000-0002-7828-3624"},"institutions":[{"id":"https://openalex.org/I2802624869","display_name":"Hereditary Disease Foundation","ror":"https://ror.org/02a3w7r50","country_code":"US","type":"other","lineage":["https://openalex.org/I2802624869"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"M. Kostrzewa","raw_affiliation_strings":["Markus Kostrzewa is a biologist and holds a PhD in Biology from the University Gie\u00dfen. After a postdoctoral position at the University Gie\u00dfen (genetic and physical mapping of a part of human chromosome 5 research in the field of hereditary neurodegenerative human disorders molecular diagnostics of hereditary human diseases), he joined Bruker Daltonik GmbH Leipzig. Currently, he is the vice R&D Manager for the areas \u201cClinical Proteomics\u201d and \u201cMicroorganism identification\u201d and Director of Molecular Biology R&D. Further, he is member of the German Society of Human Genetics and the German Society of Neurogenetics"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Markus Kostrzewa is a biologist and holds a PhD in Biology from the University Gie\u00dfen. After a postdoctoral position at the University Gie\u00dfen (genetic and physical mapping of a part of human chromosome 5 research in the field of hereditary neurodegenerative human disorders molecular diagnostics of hereditary human diseases), he joined Bruker Daltonik GmbH Leipzig. Currently, he is the vice R&D Manager for the areas \u201cClinical Proteomics\u201d and \u201cMicroorganism identification\u201d and Director of Molecular Biology R&D. Further, he is member of the German Society of Human Genetics and the German Society of Neurogenetics","institution_ids":["https://openalex.org/I2802624869"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074211261","display_name":"Axel Walch","orcid":"https://orcid.org/0000-0001-5578-4023"},"institutions":[{"id":"https://openalex.org/I17937529","display_name":"German Cancer Research Center","ror":"https://ror.org/04cdgtt98","country_code":"DE","type":"facility","lineage":["https://openalex.org/I1305996414","https://openalex.org/I17937529"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"A. Walch","raw_affiliation_strings":["Axel Walch is a pathologist and group leader of the Biomedical Microscopy Laboratory in the Institute of Pathology (Helmholtz Zentrum M\u00fcnchen, German Research Center for Environmental Health). His areas of expertise comprise quantitative pathology, cancer tissue biomarkers, esophageal cancer, gastric cancer and translation of molecular techniques to diagnostic pathology. The primary research focus is the identification of predictive and prognostic biomarkers in cancer"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Axel Walch is a pathologist and group leader of the Biomedical Microscopy Laboratory in the Institute of Pathology (Helmholtz Zentrum M\u00fcnchen, German Research Center for Environmental Health). His areas of expertise comprise quantitative pathology, cancer tissue biomarkers, esophageal cancer, gastric cancer and translation of molecular techniques to diagnostic pathology. The primary research focus is the identification of predictive and prognostic biomarkers in cancer","institution_ids":["https://openalex.org/I17937529"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091180862","display_name":"Barbara Hammer","orcid":"https://orcid.org/0000-0002-0935-5591"},"institutions":[{"id":"https://openalex.org/I294728872","display_name":"Her Majesty's Revenue and Customs","ror":"https://ror.org/00hkb6y19","country_code":"GB","type":"government","lineage":["https://openalex.org/I2802373619","https://openalex.org/I294728872"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"B. Hammer","raw_affiliation_strings":["Barbara Hammer received her PhD in Computer Science in 1995 and her venia legendi in Computer Science in 2003, both from the University of Osnabrueck, Germany. From 2000 to 2004, she was leader of the junior research group \u2018Learning with Neural Methods on Structured Data\u2019 at University of Osnabrueck. In 2004, she became professor for Theoretical Computer Science at Clausthal University of Technology, Germany. Several research stays have taken her to Italy, UK, India, France, and the USA. Her areas of expertise include hybrid systems, self-organizing maps, clustering, recurrent networks and their in bioinformatics, industrial process monitoring, or cognitive science"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Barbara Hammer received her PhD in Computer Science in 1995 and her venia legendi in Computer Science in 2003, both from the University of Osnabrueck, Germany. From 2000 to 2004, she was leader of the junior research group \u2018Learning with Neural Methods on Structured Data\u2019 at University of Osnabrueck. In 2004, she became professor for Theoretical Computer Science at Clausthal University of Technology, Germany. Several research stays have taken her to Italy, UK, India, France, and the USA. Her areas of expertise include hybrid systems, self-organizing maps, clustering, recurrent networks and their in bioinformatics, industrial process monitoring, or cognitive science","institution_ids":["https://openalex.org/I294728872"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":3885,"currency":"USD","value_usd":3885},"apc_paid":null,"fwci":5.9345,"has_fulltext":true,"cited_by_count":51,"citation_normalized_percentile":{"value":0.96569916,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"9","issue":"2","first_page":"129","last_page":"143"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12946","display_name":"Fractal and DNA sequence analysis","score":0.9846000075340271,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12254","display_name":"Machine Learning in Bioinformatics","score":0.9767000079154968,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.657426118850708},{"id":"https://openalex.org/keywords/learning-vector-quantization","display_name":"Learning vector quantization","score":0.636987566947937},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6206222772598267},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.566874086856842},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.5228462815284729},{"id":"https://openalex.org/keywords/vector-quantization","display_name":"Vector quantization","score":0.4882335960865021},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.4798373878002167},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.44899317622184753},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.42511922121047974},{"id":"https://openalex.org/keywords/neural-gas","display_name":"Neural gas","score":0.4237218499183655},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.41089385747909546},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2418062388896942}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.657426118850708},{"id":"https://openalex.org/C40567965","wikidata":"https://www.wikidata.org/wiki/Q1820283","display_name":"Learning vector quantization","level":3,"score":0.636987566947937},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6206222772598267},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.566874086856842},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.5228462815284729},{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.4882335960865021},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.4798373878002167},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.44899317622184753},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42511922121047974},{"id":"https://openalex.org/C90322556","wikidata":"https://www.wikidata.org/wiki/Q1981169","display_name":"Neural gas","level":4,"score":0.4237218499183655},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.41089385747909546},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2418062388896942},{"id":"https://openalex.org/C175202392","wikidata":"https://www.wikidata.org/wiki/Q2434543","display_name":"Time delay neural network","level":3,"score":0.0}],"mesh":[{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001419","descriptor_name":"Bacteria","qualifier_ui":"Q000145","qualifier_name":"classification","is_major_topic":false},{"descriptor_ui":"D001419","descriptor_name":"Bacteria","qualifier_ui":"Q000145","qualifier_name":"classification","is_major_topic":false},{"descriptor_ui":"D001419","descriptor_name":"Bacteria","qualifier_ui":"Q000145","qualifier_name":"classification","is_major_topic":false},{"descriptor_ui":"D001940","descriptor_name":"Breast","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001940","descriptor_name":"Breast","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001940","descriptor_name":"Breast","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003205","descriptor_name":"Computing Methodologies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003205","descriptor_name":"Computing Methodologies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003205","descriptor_name":"Computing Methodologies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003627","descriptor_name":"Data Interpretation, Statistical","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D003627","descriptor_name":"Data Interpretation, Statistical","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D003627","descriptor_name":"Data Interpretation, Statistical","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D005260","descriptor_name":"Female","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008433","descriptor_name":"Mathematics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008433","descriptor_name":"Mathematics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008433","descriptor_name":"Mathematics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D009369","descriptor_name":"Neoplasms","qualifier_ui":"Q000473","qualifier_name":"pathology","is_major_topic":false},{"descriptor_ui":"D009369","descriptor_name":"Neoplasms","qualifier_ui":"Q000473","qualifier_name":"pathology","is_major_topic":false},{"descriptor_ui":"D009369","descriptor_name":"Neoplasms","qualifier_ui":"Q000473","qualifier_name":"pathology","is_major_topic":false},{"descriptor_ui":"D013058","descriptor_name":"Mass Spectrometry","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D013058","descriptor_name":"Mass Spectrometry","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D013058","descriptor_name":"Mass Spectrometry","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D015233","descriptor_name":"Models, Statistical","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D015233","descriptor_name":"Models, Statistical","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D015233","descriptor_name":"Models, Statistical","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D017143","descriptor_name":"Fuzzy Logic","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D017143","descriptor_name":"Fuzzy Logic","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D017143","descriptor_name":"Fuzzy Logic","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D040901","descriptor_name":"Proteomics","qualifier_ui":"Q000295","qualifier_name":"instrumentation","is_major_topic":true},{"descriptor_ui":"D040901","descriptor_name":"Proteomics","qualifier_ui":"Q000295","qualifier_name":"instrumentation","is_major_topic":true},{"descriptor_ui":"D040901","descriptor_name":"Proteomics","qualifier_ui":"Q000295","qualifier_name":"instrumentation","is_major_topic":true},{"descriptor_ui":"D040901","descriptor_name":"Proteomics","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D040901","descriptor_name":"Proteomics","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true},{"descriptor_ui":"D040901","descriptor_name":"Proteomics","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":true}],"locations_count":5,"locations":[{"id":"doi:10.1093/bib/bbn009","is_oa":true,"landing_page_url":"https://doi.org/10.1093/bib/bbn009","pdf_url":"https://academic.oup.com/bib/article-pdf/9/2/129/774623/bbn009.pdf","source":{"id":"https://openalex.org/S91767247","display_name":"Briefings in Bioinformatics","issn_l":"1467-5463","issn":["1467-5463","1477-4054"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Briefings in Bioinformatics","raw_type":"journal-article"},{"id":"pmid:18334515","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/18334515","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Briefings in bioinformatics","raw_type":"Journal Article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.165.215","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.165.215","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://gaos.org/~schleif/bib_2008.pdf","raw_type":"text"},{"id":"pmh:oai:opus-zb.helmholtz-muenchen.de:3102","is_oa":false,"landing_page_url":"https://push-zb.helmholtz-muenchen.de/frontdoor.php?source_opus=3102","pdf_url":null,"source":{"id":"https://openalex.org/S4377196115","display_name":"Site cant be reached","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Brief. Bioinform. 9, 129-143 (2008)","raw_type":null},{"id":"pmh:oai:pub.librecat.org:1994253","is_oa":false,"landing_page_url":"https://pub.uni-bielefeld.de/record/1994253","pdf_url":null,"source":{"id":"https://openalex.org/S4306401671","display_name":"PUB \u2013 Publications at Bielefeld University (Bielefeld University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I20121455","host_organization_name":"Bielefeld University","host_organization_lineage":["https://openalex.org/I20121455"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Villmann T, Schleif F-M, Kostrzewa M, Walch A, Hammer B. Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods. &lt;em&gt;Briefings in Bioinformatics&lt;/em&gt;. 2008;9(2):129-143.","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1093/bib/bbn009","is_oa":true,"landing_page_url":"https://doi.org/10.1093/bib/bbn009","pdf_url":"https://academic.oup.com/bib/article-pdf/9/2/129/774623/bbn009.pdf","source":{"id":"https://openalex.org/S91767247","display_name":"Briefings in Bioinformatics","issn_l":"1467-5463","issn":["1467-5463","1477-4054"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Briefings in Bioinformatics","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.4399999976158142}],"awards":[],"funders":[{"id":"https://openalex.org/F4320325298","display_name":"Universit\u00e4t Leipzig","ror":"https://ror.org/03s7gtk40"},{"id":"https://openalex.org/F4320328265","display_name":"Deutsche Gesellschaft f\u00fcr Humangenetik","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2119942013.pdf","grobid_xml":"https://content.openalex.org/works/W2119942013.grobid-xml"},"referenced_works_count":43,"referenced_works":["https://openalex.org/W570854018","https://openalex.org/W615088007","https://openalex.org/W1482139961","https://openalex.org/W1486158543","https://openalex.org/W1489002364","https://openalex.org/W1498183065","https://openalex.org/W1503049134","https://openalex.org/W1509027411","https://openalex.org/W1540550673","https://openalex.org/W1554663460","https://openalex.org/W1563088657","https://openalex.org/W1567755388","https://openalex.org/W1589969357","https://openalex.org/W1597507633","https://openalex.org/W1617610339","https://openalex.org/W1663973292","https://openalex.org/W1664167130","https://openalex.org/W1680622244","https://openalex.org/W1990784125","https://openalex.org/W1995496685","https://openalex.org/W2021699171","https://openalex.org/W2039115800","https://openalex.org/W2047286442","https://openalex.org/W2055524657","https://openalex.org/W2057526619","https://openalex.org/W2067509137","https://openalex.org/W2084562574","https://openalex.org/W2094150678","https://openalex.org/W2112142982","https://openalex.org/W2123749980","https://openalex.org/W2125134203","https://openalex.org/W2138754805","https://openalex.org/W2144004768","https://openalex.org/W2146077544","https://openalex.org/W2150864321","https://openalex.org/W2166322089","https://openalex.org/W2170966581","https://openalex.org/W2176720124","https://openalex.org/W2799148064","https://openalex.org/W3017143921","https://openalex.org/W3119651796","https://openalex.org/W3123606858","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W1974609466","https://openalex.org/W2378212145","https://openalex.org/W1506554295","https://openalex.org/W2400327593","https://openalex.org/W2002093688","https://openalex.org/W3012423201","https://openalex.org/W3049633467","https://openalex.org/W1544367013","https://openalex.org/W3152466820","https://openalex.org/W2160422165"],"abstract_inverted_index":{"In":[0],"the":[1,12,21,43,55,69,74,100,106,121,130],"present":[2],"contribution":[3],"we":[4],"propose":[5],"two":[6,127],"recently":[7],"developed":[8],"classification":[9,86,97,131],"algorithms":[10,26,49],"for":[11,33,45,116,126],"analysis":[13],"of":[14,38,57,68,102,108,123,132],"mass-spectrometric":[15],"data-the":[16],"supervised":[17],"neural":[18],"gas":[19],"and":[20,42,85,136,138],"fuzzy-labeled":[22,75],"self-organizing":[23,76],"map.":[24],"The":[25,48],"are":[27,50],"inherently":[28],"regularizing,":[29],"which":[30,112],"is":[31,60,78],"recommended,":[32],"these":[34],"spectral":[35],"data":[36],"because":[37],"its":[39],"high":[40],"dimensionality":[41],"sparseness":[44],"specific":[46],"problems.":[47],"both":[51,124],"prototype-based":[52],"such":[53],"that":[54],"principle":[56],"characteristic":[58],"representants":[59],"realized.":[61],"This":[62],"leads":[63],"to":[64,80],"an":[65],"easy":[66],"interpretation":[67],"generated":[70],"classifcation":[71],"model.":[72],"Further,":[73],"map":[77],"able":[79],"process":[81],"uncertainty":[82],"in":[83,142],"data,":[84],"results":[87],"can":[88,113],"be":[89,114],"obtained":[90],"as":[91],"fuzzy":[92,96],"decisions.":[93],"Moreover,":[94],"this":[95],"together":[98],"with":[99],"property":[101],"topographic":[103],"mapping":[104],"offers":[105],"possibility":[107],"class":[109,117],"similarity":[110],"detection,":[111],"used":[115],"visualization.":[118],"We":[119],"demonstrate":[120],"power":[122],"methods":[125],"exemplary":[128],"examples:":[129],"bacteria":[133],"(listeria":[134],"types)":[135],"neoplastic":[137],"non-neoplastic":[139],"cell":[140],"populations":[141],"breast":[143],"cancer":[144],"tissue":[145],"sections.":[146]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":1},{"year":2015,"cited_by_count":5},{"year":2014,"cited_by_count":6},{"year":2012,"cited_by_count":2}],"updated_date":"2026-08-27T14:10:00.468798","created_date":"2025-10-10T00:00:00"}
