{"id":"https://openalex.org/W2344117088","doi":"https://doi.org/10.1080/21681163.2016.1160798","title":"Computational RSM modelling of dentate nucleus neuron 2D image surface","display_name":"Computational RSM modelling of dentate nucleus neuron 2D image surface","publication_year":2016,"publication_date":"2016-04-25","ids":{"openalex":"https://openalex.org/W2344117088","doi":"https://doi.org/10.1080/21681163.2016.1160798","mag":"2344117088"},"language":"en","primary_location":{"id":"doi:10.1080/21681163.2016.1160798","is_oa":false,"landing_page_url":"https://doi.org/10.1080/21681163.2016.1160798","pdf_url":null,"source":{"id":"https://openalex.org/S2764763012","display_name":"Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization","issn_l":"2168-1163","issn":["2168-1163","2168-1171"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"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/A5033279522","display_name":"Ivan Grbatini\u0107","orcid":null},"institutions":[{"id":"https://openalex.org/I4068193","display_name":"University of Belgrade","ror":"https://ror.org/02qsmb048","country_code":"RS","type":"education","lineage":["https://openalex.org/I4068193"]}],"countries":["RS"],"is_corresponding":true,"raw_author_name":"Ivan Grbatini\u0107","raw_affiliation_strings":["Medical Faculty, Laboratory for Digital Image Processing and Analysis, Institute for Biophysics, University of Belgrade, Belgrade, Serbia"],"affiliations":[{"raw_affiliation_string":"Medical Faculty, Laboratory for Digital Image Processing and Analysis, Institute for Biophysics, University of Belgrade, Belgrade, Serbia","institution_ids":["https://openalex.org/I4068193"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056010208","display_name":"Nemanja Rajkovi\u0107","orcid":"https://orcid.org/0000-0002-3845-3780"},"institutions":[{"id":"https://openalex.org/I4068193","display_name":"University of Belgrade","ror":"https://ror.org/02qsmb048","country_code":"RS","type":"education","lineage":["https://openalex.org/I4068193"]}],"countries":["RS"],"is_corresponding":false,"raw_author_name":"Nemanja Rajkovi\u0107","raw_affiliation_strings":["Medical Faculty, Laboratory for Digital Image Processing and Analysis, Institute for Biophysics, University of Belgrade, Belgrade, Serbia"],"affiliations":[{"raw_affiliation_string":"Medical Faculty, Laboratory for Digital Image Processing and Analysis, Institute for Biophysics, University of Belgrade, Belgrade, Serbia","institution_ids":["https://openalex.org/I4068193"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045455413","display_name":"Neboj\u0161a T. Milo\u0161evi\u0107","orcid":"https://orcid.org/0000-0002-0589-1848"},"institutions":[{"id":"https://openalex.org/I4068193","display_name":"University of Belgrade","ror":"https://ror.org/02qsmb048","country_code":"RS","type":"education","lineage":["https://openalex.org/I4068193"]}],"countries":["RS"],"is_corresponding":false,"raw_author_name":"Neboj\u0161a Milo\u0161evi\u0107","raw_affiliation_strings":["Medical Faculty, Laboratory for Digital Image Processing and Analysis, Institute for Biophysics, University of Belgrade, Belgrade, Serbia"],"affiliations":[{"raw_affiliation_string":"Medical Faculty, Laboratory for Digital Image Processing and Analysis, Institute for Biophysics, University of Belgrade, Belgrade, Serbia","institution_ids":["https://openalex.org/I4068193"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5033279522"],"corresponding_institution_ids":["https://openalex.org/I4068193"],"apc_list":null,"apc_paid":null,"fwci":0.325,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.60001974,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"6","issue":"1","first_page":"43","last_page":"50"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10581","display_name":"Neural dynamics and brain function","score":0.9815000295639038,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10581","display_name":"Neural dynamics and brain function","score":0.9815000295639038,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11601","display_name":"Neuroscience and Neural Engineering","score":0.9380000233650208,"subfield":{"id":"https://openalex.org/subfields/2804","display_name":"Cellular and Molecular Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/response-surface-methodology","display_name":"Response surface methodology","score":0.5116179585456848},{"id":"https://openalex.org/keywords/surface","display_name":"Surface (topology)","score":0.4837096333503723},{"id":"https://openalex.org/keywords/quadratic-equation","display_name":"Quadratic equation","score":0.46123552322387695},{"id":"https://openalex.org/keywords/computational-model","display_name":"Computational model","score":0.45272353291511536},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.42149391770362854},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3247818648815155},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.30115950107574463},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.29566168785095215},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.20967471599578857},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.10263660550117493},{"id":"https://openalex.org/keywords/chromatography","display_name":"Chromatography","score":0.0790812075138092},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.0698767900466919}],"concepts":[{"id":"https://openalex.org/C150077022","wikidata":"https://www.wikidata.org/wiki/Q3136137","display_name":"Response surface methodology","level":2,"score":0.5116179585456848},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.4837096333503723},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.46123552322387695},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.45272353291511536},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.42149391770362854},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3247818648815155},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30115950107574463},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29566168785095215},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.20967471599578857},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.10263660550117493},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0790812075138092},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0698767900466919}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/21681163.2016.1160798","is_oa":false,"landing_page_url":"https://doi.org/10.1080/21681163.2016.1160798","pdf_url":null,"source":{"id":"https://openalex.org/S2764763012","display_name":"Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization","issn_l":"2168-1163","issn":["2168-1163","2168-1171"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1923722","https://openalex.org/W566632165","https://openalex.org/W1968763225","https://openalex.org/W1981250673","https://openalex.org/W1984274835","https://openalex.org/W1987594626","https://openalex.org/W2011920827","https://openalex.org/W2036200796","https://openalex.org/W2039887309","https://openalex.org/W2055929917","https://openalex.org/W2070584393","https://openalex.org/W2075516160","https://openalex.org/W2076009642","https://openalex.org/W2085996360","https://openalex.org/W2086459780","https://openalex.org/W2094520929","https://openalex.org/W2183056949","https://openalex.org/W2397511970","https://openalex.org/W2506421807","https://openalex.org/W2795740701","https://openalex.org/W4229804223"],"related_works":["https://openalex.org/W2073503281","https://openalex.org/W2384332569","https://openalex.org/W3006216983","https://openalex.org/W2007957789","https://openalex.org/W4288908364","https://openalex.org/W4323538665","https://openalex.org/W1983047026","https://openalex.org/W2739498073","https://openalex.org/W2579550961","https://openalex.org/W2770776833"],"abstract_inverted_index":{"Backgrounds:":[0],"The":[1],"aim":[2],"of":[3,37,69,150],"this":[4],"study":[5],"is":[6,32,149,159],"to":[7,34,60,161],"model":[8,89,107,158],"2D":[9,169],"dentate":[10,38],"nucleus":[11,39],"neuron":[12,61,170],"surface":[13,47,171],"(2D":[14],"DNNIS)":[15],"using":[16],"the":[17,35,136,151,163],"RSM":[18],"modelling":[19,27,52,168],"method":[20],"and":[21,43,67,76,79,99,105,132],"show":[22],"in":[23,73],"general":[24,123,157],"such":[25],"a":[26],"approach.":[28],"Additionally,":[29],"an":[30],"application":[31],"made":[33],"neurons":[36],"lamina,":[40,135],"namely":[41,122],"VLL":[42,78,100],"DML.":[44],"Methods:":[45],"response":[46],"methodology":[48],"(RSM).":[49],"Results:":[50],"Several":[51],"formula":[53],"are":[54,57,65,82,87,119,138],"obtained.":[55],"Models":[56],"classified":[58],"according":[59],"samples":[62,95],"on":[63],"which":[64],"obtained":[66],"number":[68],"factors":[70],"used.":[71],"Thus,":[72],"general,":[74],"constrained":[75,156],"non-constrained,":[77],"DML":[80,112],"models":[81,86,118,137],"analysed.":[83],"Obtained":[84],"non-constrained":[85],"quadratic":[88],"with":[90,108,128,140],"multifactor":[91,109],"interaction":[92,110,127,142],"for":[93,111,133,167,172],"all":[94],"(adjusted":[96,102,114,143],"R2":[97,103,115,130,144,154],"0.96)":[98],"sample":[101,113],"0.98)":[104],"linear":[106],"0.95).":[116,145],"Constrained":[117],"bifactor":[120],"models,":[121],"one":[124,166],"without":[125],"factor":[126,141],"adjusted":[129,153],"0.93;":[131],"particular":[134],"accompanied":[139],"Conclusion:":[146],"Though":[147],"it":[148],"smallest":[152],"(0.93),":[155],"shown":[160],"be":[162],"most":[164],"promising":[165],"adult":[173],"DNN.":[174]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}