{"id":"https://openalex.org/W2104635763","doi":"https://doi.org/10.1109/icmla.2008.126","title":"Nonlinear Kernel-Based Approaches for Predicting Normal Tissue Toxicities","display_name":"Nonlinear Kernel-Based Approaches for Predicting Normal Tissue Toxicities","publication_year":2008,"publication_date":"2008-01-01","ids":{"openalex":"https://openalex.org/W2104635763","doi":"https://doi.org/10.1109/icmla.2008.126","mag":"2104635763"},"language":"en","primary_location":{"id":"doi:10.1109/icmla.2008.126","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla.2008.126","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 Seventh International Conference on Machine Learning and Applications","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/A5010597773","display_name":"Issam El Naqa","orcid":"https://orcid.org/0000-0001-6023-1132"},"institutions":[{"id":"https://openalex.org/I204465549","display_name":"Washington University in St. Louis","ror":"https://ror.org/01yc7t268","country_code":"US","type":"education","lineage":["https://openalex.org/I204465549"]},{"id":"https://openalex.org/I47838141","display_name":"Saint Louis University","ror":"https://ror.org/01p7jjy08","country_code":"US","type":"education","lineage":["https://openalex.org/I47838141"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Issam El Naqa","raw_affiliation_strings":["Department of Radiation Oncology, School of Medicine, St. Louis, MO","Department of Radiation Oncology, Washington University, Saint Louis, USA"],"affiliations":[{"raw_affiliation_string":"Department of Radiation Oncology, School of Medicine, St. Louis, MO","institution_ids":["https://openalex.org/I47838141"]},{"raw_affiliation_string":"Department of Radiation Oncology, Washington University, Saint Louis, USA","institution_ids":["https://openalex.org/I204465549"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002507883","display_name":"Jeffrey D. Bradley","orcid":"https://orcid.org/0000-0003-3047-3151"},"institutions":[{"id":"https://openalex.org/I204465549","display_name":"Washington University in St. Louis","ror":"https://ror.org/01yc7t268","country_code":"US","type":"education","lineage":["https://openalex.org/I204465549"]},{"id":"https://openalex.org/I47838141","display_name":"Saint Louis University","ror":"https://ror.org/01p7jjy08","country_code":"US","type":"education","lineage":["https://openalex.org/I47838141"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jeffrey D. Bradley","raw_affiliation_strings":["Department of Radiation Oncology, School of Medicine, St. Louis, MO","Department of Radiation Oncology, Washington University, Saint Louis, USA"],"affiliations":[{"raw_affiliation_string":"Department of Radiation Oncology, School of Medicine, St. Louis, MO","institution_ids":["https://openalex.org/I47838141"]},{"raw_affiliation_string":"Department of Radiation Oncology, Washington University, Saint Louis, USA","institution_ids":["https://openalex.org/I204465549"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026159944","display_name":"Joseph O. Deasy","orcid":"https://orcid.org/0000-0002-9437-266X"},"institutions":[{"id":"https://openalex.org/I204465549","display_name":"Washington University in St. Louis","ror":"https://ror.org/01yc7t268","country_code":"US","type":"education","lineage":["https://openalex.org/I204465549"]},{"id":"https://openalex.org/I47838141","display_name":"Saint Louis University","ror":"https://ror.org/01p7jjy08","country_code":"US","type":"education","lineage":["https://openalex.org/I47838141"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joseph O. Deasy","raw_affiliation_strings":["Department of Radiation Oncology, School of Medicine, St. Louis, MO","Department of Radiation Oncology, Washington University, Saint Louis, USA"],"affiliations":[{"raw_affiliation_string":"Department of Radiation Oncology, School of Medicine, St. Louis, MO","institution_ids":["https://openalex.org/I47838141"]},{"raw_affiliation_string":"Department of Radiation Oncology, Washington University, Saint Louis, USA","institution_ids":["https://openalex.org/I204465549"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5010597773"],"corresponding_institution_ids":["https://openalex.org/I204465549","https://openalex.org/I47838141"],"apc_list":null,"apc_paid":null,"fwci":4.1054,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.93785287,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"539","last_page":"544"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.996999979019165,"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/T10862","display_name":"AI in cancer detection","score":0.996999979019165,"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/T10885","display_name":"Gene expression and cancer classification","score":0.98580002784729,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.973800003528595,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6181602478027344},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.5990259647369385},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5933992266654968},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5720208883285522},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5655021667480469},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5417280197143555},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.4944639205932617},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.4297768771648407},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4133192002773285},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4131573438644409},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2815915644168854}],"concepts":[{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6181602478027344},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.5990259647369385},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5933992266654968},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5720208883285522},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5655021667480469},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5417280197143555},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.4944639205932617},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4297768771648407},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4133192002773285},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4131573438644409},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2815915644168854},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmla.2008.126","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla.2008.126","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 Seventh International Conference on Machine Learning and Applications","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6700000166893005,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W126933578","https://openalex.org/W1495297318","https://openalex.org/W1510073064","https://openalex.org/W1540861017","https://openalex.org/W1554944419","https://openalex.org/W1964052391","https://openalex.org/W1965654523","https://openalex.org/W1971605653","https://openalex.org/W1976999319","https://openalex.org/W2024432515","https://openalex.org/W2032477787","https://openalex.org/W2045678013","https://openalex.org/W2050256381","https://openalex.org/W2052467512","https://openalex.org/W2053029299","https://openalex.org/W2079525162","https://openalex.org/W2117204935","https://openalex.org/W2119479037","https://openalex.org/W2124258418","https://openalex.org/W2133458583","https://openalex.org/W2137190664","https://openalex.org/W2143426320","https://openalex.org/W2148603752","https://openalex.org/W2171033594","https://openalex.org/W3023596864","https://openalex.org/W3113059896","https://openalex.org/W4247105260","https://openalex.org/W4285719527","https://openalex.org/W4298870207"],"related_works":["https://openalex.org/W2052515325","https://openalex.org/W2050948537","https://openalex.org/W2767646790","https://openalex.org/W2352041579","https://openalex.org/W1488006380","https://openalex.org/W2138381686","https://openalex.org/W2141585124","https://openalex.org/W1998176685","https://openalex.org/W3123883311","https://openalex.org/W2032175896"],"abstract_inverted_index":{"Since":[0],"the":[1,5,131,143],"early":[2],"demonstration":[3],"of":[4,8,24,31,59,67,87],"curative":[6],"potential":[7],"radiation":[9],"therapy":[10],"for":[11,43,85,95,111,117],"tumor":[12],"sterilization,":[13],"normal":[14],"tissue":[15],"toxicity":[16],"continues":[17],"to":[18,136,142],"be":[19],"dose":[20],"limiting.":[21],"Accurate":[22],"prediction":[23,139],"patient\u00bfs":[25],"complication":[26],"risk":[27],"would":[28,63],"allow":[29],"personalization":[30],"treatment":[32],"planning":[33],"decisions.":[34],"Nonlinear":[35],"kernel":[36,120],"methods":[37,62,84,129],"can":[38],"provide":[39],"a":[40,68,108],"robust":[41],"framework":[42,134],"learning":[44],"complex":[45],"interactions":[46],"between":[47],"observed":[48],"toxicities":[49],"and":[50,53,91,116],"treatment,":[51],"anatomical,":[52],"patient-related":[54],"variables.":[55,78],"However,":[56],"proper":[57,119],"application":[58],"these":[60,77],"powerful":[61],"require":[64],"better":[65],"understanding":[66],"high-dimensional":[69,89],"feature":[70],"space":[71,90],"that":[72,103],"is":[73,107],"spanned":[74],"by":[75],"all":[76],"In":[79,122],"this":[80,88],"work,":[81],"we":[82],"investigate":[83],"visualization":[86],"compare":[92],"different":[93],"approaches":[94],"extracting":[96],"discriminant":[97],"features.":[98],"Our":[99],"preliminary":[100],"results":[101],"demonstrate":[102],"principle":[104],"component":[105],"analysis":[106],"valuable":[109],"tool":[110],"visualizing":[112],"high":[113],"dimensional":[114],"data":[115],"determining":[118],"type.":[121],"addition,":[123],"variable":[124],"selection":[125],"based":[126],"on":[127],"resampling":[128],"within":[130],"logistic":[132],"regression":[133],"seemed":[135],"yield":[137],"improved":[138],"performance":[140],"compared":[141],"recursive-feature":[144],"elimination":[145],"method.":[146]},"counts_by_year":[{"year":2023,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2012,"cited_by_count":2}],"updated_date":"2026-04-17T18:11:37.981687","created_date":"2025-10-10T00:00:00"}
