{"id":"https://openalex.org/W4387782121","doi":"https://doi.org/10.1186/s12859-023-05526-3","title":"Designs for the simultaneous inference of concentration\u2013response curves","display_name":"Designs for the simultaneous inference of concentration\u2013response curves","publication_year":2023,"publication_date":"2023-10-19","ids":{"openalex":"https://openalex.org/W4387782121","doi":"https://doi.org/10.1186/s12859-023-05526-3","pmid":"https://pubmed.ncbi.nlm.nih.gov/37858091"},"language":"en","primary_location":{"id":"doi:10.1186/s12859-023-05526-3","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1186/s12859-023-05526-3","pdf_url":"https://link.springer.com/content/pdf/10.1186/s12859-023-05526-3.pdf","source":{"id":"https://openalex.org/S19032547","display_name":"BMC Bioinformatics","issn_l":"1471-2105","issn":["1471-2105"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC Bioinformatics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://link.springer.com/content/pdf/10.1186/s12859-023-05526-3.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5003442133","display_name":"Leonie Sch\u00fcrmeyer","orcid":"https://orcid.org/0000-0001-9811-3641"},"institutions":[{"id":"https://openalex.org/I200332995","display_name":"TU Dortmund University","ror":"https://ror.org/01k97gp34","country_code":"DE","type":"education","lineage":["https://openalex.org/I200332995"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Leonie Sch\u00fcrmeyer","raw_affiliation_strings":["Department of Statistics, TU Dortmund University, Dortmund, Germany. schuermeyer@statistik.tu-dortmund.de","Department of Statistics, TU Dortmund University, Dortmund, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, TU Dortmund University, Dortmund, Germany. schuermeyer@statistik.tu-dortmund.de","institution_ids":["https://openalex.org/I200332995"]},{"raw_affiliation_string":"Department of Statistics, TU Dortmund University, Dortmund, Germany","institution_ids":["https://openalex.org/I200332995"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013007833","display_name":"Kirsten Schorning","orcid":"https://orcid.org/0000-0002-9401-5486"},"institutions":[{"id":"https://openalex.org/I200332995","display_name":"TU Dortmund University","ror":"https://ror.org/01k97gp34","country_code":"DE","type":"education","lineage":["https://openalex.org/I200332995"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Kirsten Schorning","raw_affiliation_strings":["Department of Statistics, TU Dortmund University, Dortmund, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, TU Dortmund University, Dortmund, Germany","institution_ids":["https://openalex.org/I200332995"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075610512","display_name":"J\u00f6rg Rahnenf\u00fchrer","orcid":"https://orcid.org/0000-0002-8947-440X"},"institutions":[{"id":"https://openalex.org/I200332995","display_name":"TU Dortmund University","ror":"https://ror.org/01k97gp34","country_code":"DE","type":"education","lineage":["https://openalex.org/I200332995"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"J\u00f6rg Rahnenf\u00fchrer","raw_affiliation_strings":["Department of Statistics, TU Dortmund University, Dortmund, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, TU Dortmund University, Dortmund, Germany","institution_ids":["https://openalex.org/I200332995"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5003442133"],"corresponding_institution_ids":["https://openalex.org/I200332995"],"apc_list":{"value":2790,"currency":"USD","value_usd":2790},"apc_paid":{"value":1889,"currency":"EUR","value_usd":2037},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.15969912,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"24","issue":"1","first_page":"393","last_page":"393"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11798","display_name":"Optimal Experimental Design Methods","score":0.3547999858856201,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11798","display_name":"Optimal Experimental Design Methods","score":0.3547999858856201,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10885","display_name":"Gene expression and cancer classification","score":0.3456000089645386,"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/T11235","display_name":"Statistical Methods in Clinical Trials","score":0.1386999934911728,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.8315845727920532},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.6331121325492859},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6133774518966675},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5608209371566772},{"id":"https://openalex.org/keywords/design-of-experiments","display_name":"Design of experiments","score":0.5142919421195984},{"id":"https://openalex.org/keywords/statistical-inference","display_name":"Statistical inference","score":0.49711063504219055},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4099433422088623},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2689337432384491},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2111164927482605},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.20444226264953613}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.8315845727920532},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.6331121325492859},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6133774518966675},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5608209371566772},{"id":"https://openalex.org/C34559072","wikidata":"https://www.wikidata.org/wiki/Q2334061","display_name":"Design of experiments","level":2,"score":0.5142919421195984},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.49711063504219055},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4099433422088623},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2689337432384491},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2111164927482605},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.20444226264953613},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[{"descriptor_ui":"D003198","descriptor_name":"Computer Simulation","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003198","descriptor_name":"Computer Simulation","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003198","descriptor_name":"Computer Simulation","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D012107","descriptor_name":"Research Design","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D012107","descriptor_name":"Research Design","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D012107","descriptor_name":"Research Design","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":4,"locations":[{"id":"doi:10.1186/s12859-023-05526-3","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1186/s12859-023-05526-3","pdf_url":"https://link.springer.com/content/pdf/10.1186/s12859-023-05526-3.pdf","source":{"id":"https://openalex.org/S19032547","display_name":"BMC Bioinformatics","issn_l":"1471-2105","issn":["1471-2105"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC Bioinformatics","raw_type":"journal-article"},{"id":"pmid:37858091","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37858091","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":"BMC bioinformatics","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:10588042","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10588042","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10588042/pdf/12859_2023_Article_5526.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"BMC Bioinformatics","raw_type":"Text"},{"id":"pmh:oai:doaj.org/article:07aba6c788e244269f4476d0232db5e8","is_oa":true,"landing_page_url":"https://doaj.org/article/07aba6c788e244269f4476d0232db5e8","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"BMC Bioinformatics, Vol 24, Iss 1, Pp 1-21 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1186/s12859-023-05526-3","is_oa":true,"landing_page_url":"http://dx.doi.org/10.1186/s12859-023-05526-3","pdf_url":"https://link.springer.com/content/pdf/10.1186/s12859-023-05526-3.pdf","source":{"id":"https://openalex.org/S19032547","display_name":"BMC Bioinformatics","issn_l":"1471-2105","issn":["1471-2105"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320256","host_organization_name":"BioMed Central","host_organization_lineage":["https://openalex.org/P4310320256","https://openalex.org/P4310319965"],"host_organization_lineage_names":["BioMed Central","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"BMC Bioinformatics","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2493638443","display_name":"GRK 2624: Biostatistische Methoden f\u00fcr hochdimensionale Daten in der Toxikologie","funder_award_id":"427806116","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G3494183208","display_name":null,"funder_award_id":"RTG 2624","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"}],"funders":[{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"},{"id":"https://openalex.org/F4320329559","display_name":"Technische Universit\u00e4t Dortmund","ror":"https://ror.org/01k97gp34"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4387782121.pdf","grobid_xml":"https://content.openalex.org/works/W4387782121.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W74630330","https://openalex.org/W609131506","https://openalex.org/W1870637160","https://openalex.org/W1965107569","https://openalex.org/W1989527938","https://openalex.org/W1991671862","https://openalex.org/W1999372955","https://openalex.org/W2014018052","https://openalex.org/W2017351261","https://openalex.org/W2019657347","https://openalex.org/W2020327222","https://openalex.org/W2030285958","https://openalex.org/W2045949646","https://openalex.org/W2048144055","https://openalex.org/W2058247645","https://openalex.org/W2069350303","https://openalex.org/W2082382318","https://openalex.org/W2093975844","https://openalex.org/W2124562077","https://openalex.org/W2152195021","https://openalex.org/W2337620333","https://openalex.org/W2734809522","https://openalex.org/W2766283631","https://openalex.org/W2799609547","https://openalex.org/W2963512824","https://openalex.org/W2964231435","https://openalex.org/W3044153450","https://openalex.org/W3125922066","https://openalex.org/W3196767092","https://openalex.org/W4200075411","https://openalex.org/W4206467103","https://openalex.org/W4212974063","https://openalex.org/W4230822461","https://openalex.org/W4253484285","https://openalex.org/W4283579831","https://openalex.org/W4300031234","https://openalex.org/W4309247346","https://openalex.org/W6618645690","https://openalex.org/W6644682428"],"related_works":["https://openalex.org/W2055243143","https://openalex.org/W4321636575","https://openalex.org/W2357796999","https://openalex.org/W2045526782","https://openalex.org/W2741131631","https://openalex.org/W137830373","https://openalex.org/W3000984192","https://openalex.org/W4286952477","https://openalex.org/W4321348134","https://openalex.org/W2103073163"],"abstract_inverted_index":{"BACKGROUND:":[0],"An":[1],"important":[2],"problem":[3,81],"in":[4,6,39,172,177,243,248],"toxicology":[5],"the":[7,14,27,32,37,42,53,64,69,87,115,119,124,137,158,161,165,180,183,197,210,217,220,229,244,251,254,261],"context":[8],"of":[9,17,21,26,34,36,44,72,90,94,118,123,133,140,144,160,167,174,179,182,219,250,253],"gene":[10,276],"expression":[11,277],"data":[12,190],"is":[13,75],"simultaneous":[15,88,107,138,214,255,265],"inference":[16,28,89,108,139,215,218,266],"a":[18,91,103,110,131,141,148,188,236],"large":[19,92,142],"number":[20,93,143],"concentration-response":[22,95,145,222],"relationships.":[23],"The":[24,207,225],"quality":[25,181,198,252],"substantially":[29,149],"depends":[30],"on":[31,41,228,258],"choice":[33],"design":[35,208,226,263],"experiments,":[38],"particular,":[40,155],"set":[43,60],"different":[45,54,221],"concentrations,":[46],"at":[47],"which":[48,113,239],"observations":[49],"are":[50],"taken":[51],"for":[52,66,86,106,213,264,270],"genes":[55],"under":[56],"consideration.":[57],"As":[58],"this":[59,80],"has":[61],"to":[62,164],"be":[63,268],"same":[65],"all":[67],"genes,":[68],"efficient":[70,84],"planning":[71,132],"such":[73],"experiments":[74,134],"very":[76],"challenging.":[77],"We":[78,128],"address":[79],"by":[82],"determining":[83],"designs":[85,122,163,171],"models.":[96,126],"For":[97,196],"that":[98,130,135],"purpose,":[99],"we":[100,156,200],"both":[101],"construct":[102],"D-optimality":[104,211],"criterion":[105,212],"and":[109,176],"K-means":[111,230],"procedure":[112,231],"clusters":[114],"support":[116],"points":[117],"locally":[120],"D-optimal":[121,262],"individual":[125],"RESULTS:":[127],"show":[129],"addresses":[136],"relationships":[146,223],"yields":[147],"more":[150],"accurate":[151],"statistical":[152],"analysis.":[153],"In":[154],"compare":[157],"performance":[159],"constructed":[162],"ones":[166],"other":[168],"commonly":[169],"used":[170,269],"terms":[173,178,249],"D-efficiencies":[175],"resulting":[184],"model":[185],"fits":[186],"using":[187],"real":[189],"example":[191],"dealing":[192,273],"with":[193,274],"valproic":[194],"acid.":[195],"comparison":[199],"perform":[201],"an":[202],"extensive":[203],"simulation":[204],"study.":[205],"CONCLUSIONS:":[206],"maximizing":[209],"improves":[216],"substantially.":[224],"based":[227],"also":[232,241],"performs":[233,246],"well,":[234],"whereas":[235],"log-equidistant":[237],"design,":[238],"was":[240],"included":[242],"analysis,":[245],"poorly":[247],"inference.":[256],"Based":[257],"our":[259],"findings,":[260],"should":[267],"upcoming":[271],"analyses":[272],"high-dimensional":[275],"data.":[278]},"counts_by_year":[],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}