{"id":"https://openalex.org/W1501627133","doi":"https://doi.org/10.1109/ijcnn.2005.1556313","title":"Using domain knowledge to constrain structure learning in a Bayesian bioagent detector","display_name":"Using domain knowledge to constrain structure learning in a Bayesian bioagent detector","publication_year":2006,"publication_date":"2006-01-05","ids":{"openalex":"https://openalex.org/W1501627133","doi":"https://doi.org/10.1109/ijcnn.2005.1556313","mag":"1501627133"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2005.1556313","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2005.1556313","pdf_url":null,"source":{"id":"https://openalex.org/S4363609022","display_name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088297975","display_name":"Anshu Saksena","orcid":null},"institutions":[{"id":"https://openalex.org/I2802946424","display_name":"Johns Hopkins University Applied Physics Laboratory","ror":"https://ror.org/029pp9z10","country_code":"US","type":"facility","lineage":["https://openalex.org/I145311948","https://openalex.org/I2802946424"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"A. Saksena","raw_affiliation_strings":["Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, USA","Appl. Phys. Lab. Johns Hopkins Univ., Laurel, MD, USA"],"affiliations":[{"raw_affiliation_string":"Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, USA","institution_ids":["https://openalex.org/I2802946424"]},{"raw_affiliation_string":"Appl. Phys. Lab. Johns Hopkins Univ., Laurel, MD, USA","institution_ids":["https://openalex.org/I2802946424"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037711463","display_name":"Dennis Lucarelli","orcid":"https://orcid.org/0000-0001-7863-4538"},"institutions":[{"id":"https://openalex.org/I2802946424","display_name":"Johns Hopkins University Applied Physics Laboratory","ror":"https://ror.org/029pp9z10","country_code":"US","type":"facility","lineage":["https://openalex.org/I145311948","https://openalex.org/I2802946424"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"D. Lucarelli","raw_affiliation_strings":["Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, USA","Appl. Phys. Lab. Johns Hopkins Univ., Laurel, MD, USA"],"affiliations":[{"raw_affiliation_string":"Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, USA","institution_ids":["https://openalex.org/I2802946424"]},{"raw_affiliation_string":"Appl. Phys. Lab. Johns Hopkins Univ., Laurel, MD, USA","institution_ids":["https://openalex.org/I2802946424"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109618222","display_name":"I-Jeng Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I2802946424","display_name":"Johns Hopkins University Applied Physics Laboratory","ror":"https://ror.org/029pp9z10","country_code":"US","type":"facility","lineage":["https://openalex.org/I145311948","https://openalex.org/I2802946424"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"I-Jeng Wang","raw_affiliation_strings":["Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, USA","Appl. Phys. Lab. Johns Hopkins Univ., Laurel, MD, USA"],"affiliations":[{"raw_affiliation_string":"Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, USA","institution_ids":["https://openalex.org/I2802946424"]},{"raw_affiliation_string":"Appl. Phys. Lab. Johns Hopkins Univ., Laurel, MD, USA","institution_ids":["https://openalex.org/I2802946424"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5088297975"],"corresponding_institution_ids":["https://openalex.org/I2802946424"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.10441486,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"4","issue":null,"first_page":"2601","last_page":"2606"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9779000282287598,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9779000282287598,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10836","display_name":"Metabolomics and Mass Spectrometry Studies","score":0.9593999981880188,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9488999843597412,"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/probabilistic-logic","display_name":"Probabilistic logic","score":0.7436710000038147},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6451078653335571},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6048847436904907},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5845057964324951},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5409797430038452},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.514940083026886},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.4963379502296448},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48872995376586914},{"id":"https://openalex.org/keywords/mass-spectrometry","display_name":"Mass spectrometry","score":0.452340304851532},{"id":"https://openalex.org/keywords/domain-knowledge","display_name":"Domain knowledge","score":0.41384053230285645},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4106355905532837},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.33995771408081055},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15371593832969666},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.11655169725418091},{"id":"https://openalex.org/keywords/chromatography","display_name":"Chromatography","score":0.06873908638954163}],"concepts":[{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.7436710000038147},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6451078653335571},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6048847436904907},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5845057964324951},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5409797430038452},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.514940083026886},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.4963379502296448},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48872995376586914},{"id":"https://openalex.org/C162356407","wikidata":"https://www.wikidata.org/wiki/Q180809","display_name":"Mass spectrometry","level":2,"score":0.452340304851532},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.41384053230285645},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4106355905532837},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.33995771408081055},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15371593832969666},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.11655169725418091},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.06873908638954163},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2005.1556313","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2005.1556313","pdf_url":null,"source":{"id":"https://openalex.org/S4363609022","display_name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320310145","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W85461036","https://openalex.org/W171629430","https://openalex.org/W1505009765","https://openalex.org/W1566045017","https://openalex.org/W1593793857","https://openalex.org/W1615454278","https://openalex.org/W1817561967","https://openalex.org/W1980619116","https://openalex.org/W2045618177","https://openalex.org/W2049633694","https://openalex.org/W2101736851","https://openalex.org/W2169415915","https://openalex.org/W2594671650","https://openalex.org/W6630118112","https://openalex.org/W6633761756"],"related_works":["https://openalex.org/W2494523064","https://openalex.org/W2943623134","https://openalex.org/W2215759665","https://openalex.org/W2960358116","https://openalex.org/W2030292806","https://openalex.org/W3041172967","https://openalex.org/W2749065928","https://openalex.org/W2147155098","https://openalex.org/W4287727129","https://openalex.org/W2938171715"],"abstract_inverted_index":{"A":[0],"novel":[1],"procedure":[2],"for":[3,14],"learning":[4,25],"a":[5],"probabilistic":[6,36],"model":[7,37],"from":[8,41],"mass":[9,42],"spectrometry":[10,43],"data":[11],"that":[12],"accounts":[13],"domain":[15],"specific":[16],"noise":[17],"and":[18],"mitigates":[19],"the":[20,30,34],"complexity":[21],"of":[22],"Bayesian":[23],"structure":[24],"is":[26],"presented.":[27],"We":[28],"evaluate":[29],"algorithm":[31],"by":[32],"applying":[33],"learned":[35],"to":[38],"microorganism":[39],"detection":[40],"data.":[44]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
