{"id":"https://openalex.org/W4379365259","doi":"https://doi.org/10.1145/3583788.3583807","title":"Quantitative Structure-Activity Relationships of Estrogen Receptor Alpha Based on Molecular Descriptors Selection and Extreme Gradient Boosting","display_name":"Quantitative Structure-Activity Relationships of Estrogen Receptor Alpha Based on Molecular Descriptors Selection and Extreme Gradient Boosting","publication_year":2023,"publication_date":"2023-01-05","ids":{"openalex":"https://openalex.org/W4379365259","doi":"https://doi.org/10.1145/3583788.3583807"},"language":"en","primary_location":{"id":"doi:10.1145/3583788.3583807","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3583788.3583807","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 The 7th International Conference on Machine Learning and Soft Computing (ICMLSC)","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/A5080081673","display_name":"Shaotong Liu","orcid":"https://orcid.org/0000-0001-9056-0113"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Shaotong Liu","raw_affiliation_strings":["Beijing Institute of Computer Technology and Applications, China"],"raw_orcid":"https://orcid.org/0000-0001-9056-0113","affiliations":[{"raw_affiliation_string":"Beijing Institute of Computer Technology and Applications, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073136550","display_name":"Zhewei Xu","orcid":"https://orcid.org/0000-0003-4718-6175"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhewei Xu","raw_affiliation_strings":["Beijing Institute of Computer Technology and Applications, China"],"raw_orcid":"https://orcid.org/0000-0003-4718-6175","affiliations":[{"raw_affiliation_string":"Beijing Institute of Computer Technology and Applications, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102824364","display_name":"Dongsheng Ye","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dongsheng Ye","raw_affiliation_strings":["Beijing Institute of Computer Technology and Applications, China"],"raw_orcid":"https://orcid.org/0000-0001-6056-2265","affiliations":[{"raw_affiliation_string":"Beijing Institute of Computer Technology and Applications, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5080081673"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.194,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.52081121,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"132","last_page":"138"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T10211","display_name":"Computational Drug Discovery Methods","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T10908","display_name":"Analytical Chemistry and Chromatography","score":0.9628999829292297,"subfield":{"id":"https://openalex.org/subfields/1607","display_name":"Spectroscopy"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10274","display_name":"Synthesis and biological activity","score":0.9557999968528748,"subfield":{"id":"https://openalex.org/subfields/1605","display_name":"Organic Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/quantitative-structure\u2013activity-relationship","display_name":"Quantitative structure\u2013activity relationship","score":0.8153897523880005},{"id":"https://openalex.org/keywords/molecular-descriptor","display_name":"Molecular descriptor","score":0.6791423559188843},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.639774739742279},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6163735389709473},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5932539701461792},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.57358717918396},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5452708005905151},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.44181764125823975},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3733571469783783},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35561513900756836},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.08383023738861084}],"concepts":[{"id":"https://openalex.org/C164126121","wikidata":"https://www.wikidata.org/wiki/Q766383","display_name":"Quantitative structure\u2013activity relationship","level":2,"score":0.8153897523880005},{"id":"https://openalex.org/C164923092","wikidata":"https://www.wikidata.org/wiki/Q3705921","display_name":"Molecular descriptor","level":3,"score":0.6791423559188843},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.639774739742279},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6163735389709473},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5932539701461792},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.57358717918396},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5452708005905151},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.44181764125823975},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3733571469783783},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35561513900756836},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.08383023738861084},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3583788.3583807","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3583788.3583807","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 The 7th International Conference on Machine Learning and Soft Computing (ICMLSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8600000143051147,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1988195734","https://openalex.org/W2126879040","https://openalex.org/W2295598076","https://openalex.org/W2325310505","https://openalex.org/W2605952223","https://openalex.org/W3190247062","https://openalex.org/W3198294617","https://openalex.org/W4226364753"],"related_works":["https://openalex.org/W3082059448","https://openalex.org/W2145188897","https://openalex.org/W4313640622","https://openalex.org/W2148009427","https://openalex.org/W1518515965","https://openalex.org/W1978931025","https://openalex.org/W3159291941","https://openalex.org/W2063120953","https://openalex.org/W2753230657","https://openalex.org/W2165637039"],"abstract_inverted_index":{"Quantitative":[0],"Structure-Activity":[1],"Relationships":[2],"(QSAR),":[3],"which":[4,67,93],"aims":[5],"to":[6,37,71,96,109,132,150,174],"estimate":[7],"the":[8,27,38,43,55,61,111,114,137,145,152,175],"estrogen":[9],"receptor":[10],"alpha":[11],"(ER\u03b1)":[12],"activity":[13],"of":[14,29,40,45,57,63,98,116],"compounds":[15],"through":[16],"their":[17],"chemical":[18],"features":[19,147],"and":[20,86,119,125,135,140,179],"ER\u03b1,":[21],"is":[22,50,107],"a":[23,46,51,104],"fundamental":[24],"part":[25],"in":[26,60,113],"process":[28],"drug":[30],"discovery":[31],"for":[32],"breast":[33],"cancer":[34],"treatment.":[35],"Due":[36],"variety":[39],"data":[41],"properties,":[42],"building":[44],"suitable":[47],"QSAR":[48,58],"model":[49],"challenging":[52],"task.":[53],"Meanwhile,":[54],"challenge":[56],"lies":[59],"complexity":[62],"compound":[64],"molecular":[65,74,79,99,142,168],"descriptors":[66,80,169],"make":[68],"it":[69],"difficult":[70],"screen":[72],"robust":[73,141],"descriptors.":[75,100,143],"Previous":[76],"studies":[77],"select":[78,136],"manually":[81],"based":[82],"on":[83],"expert":[84],"knowledge":[85],"experience.":[87],"However,":[88],"they":[89],"are":[90,130,148],"highly":[91],"subjective":[92],"could":[94],"lead":[95],"ineffectiveness":[97],"In":[101],"this":[102],"paper,":[103],"novel":[105],"approach":[106,182],"presented":[108],"address":[110],"problems":[112],"context":[115],"regression":[117,181],"modelling":[118],"feature":[120],"selection.":[121],"Firstly,":[122],"two":[123,126],"filtered":[124],"embedded":[127],"scoring":[128],"metrics":[129],"proposed":[131],"jointly":[133],"sort":[134],"most":[138],"relevant":[139],"Then":[144],"selected":[146,167],"used":[149],"build":[151],"supervised":[153],"data-driven":[154],"model,":[155],"namely":[156],"eXtreme":[157],"Gradient":[158],"Boosting":[159],"(XGBoost)":[160],"algorithm.":[161],"Experimental":[162],"results":[163],"show":[164],"that":[165],"our":[166,180],"can":[170],"give":[171],"good":[172],"predictions":[173],"target":[176],"ER\u03b1":[177],"bioactivity":[178],"outperform":[183],"formal":[184],"models.":[185]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}