{"id":"https://openalex.org/W4413825730","doi":"https://doi.org/10.3390/make7030091","title":"Machine Learning Prediction Models of Beneficial and Toxicological Effects of Zinc Oxide Nanoparticles in Rat Feed","display_name":"Machine Learning Prediction Models of Beneficial and Toxicological Effects of Zinc Oxide Nanoparticles in Rat Feed","publication_year":2025,"publication_date":"2025-08-29","ids":{"openalex":"https://openalex.org/W4413825730","doi":"https://doi.org/10.3390/make7030091"},"language":"en","primary_location":{"id":"doi:10.3390/make7030091","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7030091","pdf_url":"https://www.mdpi.com/2504-4990/7/3/91/pdf?version=1756464529","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2504-4990/7/3/91/pdf?version=1756464529","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064536762","display_name":"Leonid Legashev","orcid":"https://orcid.org/0000-0001-6351-404X"},"institutions":[{"id":"https://openalex.org/I196486018","display_name":"Orenburg State University","ror":"https://ror.org/05b0f8e85","country_code":"RU","type":"education","lineage":["https://openalex.org/I196486018"]}],"countries":["RU"],"is_corresponding":true,"raw_author_name":"Leonid Legashev","raw_affiliation_strings":["Research Institute of Digital Intelligent Technologies, Orenburg State University, Pobedy Pr. 13, Orenburg 460018, Russia"],"raw_orcid":"https://orcid.org/0000-0001-6351-404X","affiliations":[{"raw_affiliation_string":"Research Institute of Digital Intelligent Technologies, Orenburg State University, Pobedy Pr. 13, Orenburg 460018, Russia","institution_ids":["https://openalex.org/I196486018"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068202379","display_name":"Ivan Khokhlov","orcid":"https://orcid.org/0000-0001-8848-1416"},"institutions":[{"id":"https://openalex.org/I196486018","display_name":"Orenburg State University","ror":"https://ror.org/05b0f8e85","country_code":"RU","type":"education","lineage":["https://openalex.org/I196486018"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Ivan Khokhlov","raw_affiliation_strings":["Research Institute of Digital Intelligent Technologies, Orenburg State University, Pobedy Pr. 13, Orenburg 460018, Russia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Institute of Digital Intelligent Technologies, Orenburg State University, Pobedy Pr. 13, Orenburg 460018, Russia","institution_ids":["https://openalex.org/I196486018"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081831339","display_name":"Irina Bolodurina","orcid":"https://orcid.org/0000-0003-0096-2587"},"institutions":[{"id":"https://openalex.org/I196486018","display_name":"Orenburg State University","ror":"https://ror.org/05b0f8e85","country_code":"RU","type":"education","lineage":["https://openalex.org/I196486018"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Irina Bolodurina","raw_affiliation_strings":["Research Institute of Digital Intelligent Technologies, Orenburg State University, Pobedy Pr. 13, Orenburg 460018, Russia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Institute of Digital Intelligent Technologies, Orenburg State University, Pobedy Pr. 13, Orenburg 460018, Russia","institution_ids":["https://openalex.org/I196486018"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050303499","display_name":"Alexander Shukhman","orcid":"https://orcid.org/0000-0003-2061-9102"},"institutions":[{"id":"https://openalex.org/I196486018","display_name":"Orenburg State University","ror":"https://ror.org/05b0f8e85","country_code":"RU","type":"education","lineage":["https://openalex.org/I196486018"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Alexander Shukhman","raw_affiliation_strings":["Research Institute of Digital Intelligent Technologies, Orenburg State University, Pobedy Pr. 13, Orenburg 460018, Russia"],"raw_orcid":"https://orcid.org/0000-0003-2061-9102","affiliations":[{"raw_affiliation_string":"Research Institute of Digital Intelligent Technologies, Orenburg State University, Pobedy Pr. 13, Orenburg 460018, Russia","institution_ids":["https://openalex.org/I196486018"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5106163786","display_name":"Svetlana Kolesnik","orcid":"https://orcid.org/0009-0009-3008-0308"},"institutions":[{"id":"https://openalex.org/I196486018","display_name":"Orenburg State University","ror":"https://ror.org/05b0f8e85","country_code":"RU","type":"education","lineage":["https://openalex.org/I196486018"]}],"countries":["RU"],"is_corresponding":false,"raw_author_name":"Svetlana Kolesnik","raw_affiliation_strings":["Research Institute of Digital Intelligent Technologies, Orenburg State University, Pobedy Pr. 13, Orenburg 460018, Russia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Research Institute of Digital Intelligent Technologies, Orenburg State University, Pobedy Pr. 13, Orenburg 460018, Russia","institution_ids":["https://openalex.org/I196486018"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5064536762"],"corresponding_institution_ids":["https://openalex.org/I196486018"],"apc_list":{"value":1400,"currency":"CHF","value_usd":1515},"apc_paid":{"value":1400,"currency":"CHF","value_usd":1515},"fwci":0.8113,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.72002409,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"7","issue":"3","first_page":"91","last_page":"91"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10079","display_name":"Nanoparticles: synthesis and applications","score":0.9359999895095825,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10079","display_name":"Nanoparticles: synthesis and applications","score":0.9359999895095825,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/zinc","display_name":"Zinc","score":0.7008594274520874},{"id":"https://openalex.org/keywords/nanoparticle","display_name":"Nanoparticle","score":0.6687725186347961},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43982982635498047},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3414519131183624},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3357982635498047},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.2865830063819885},{"id":"https://openalex.org/keywords/nanotechnology","display_name":"Nanotechnology","score":0.2814505398273468},{"id":"https://openalex.org/keywords/metallurgy","display_name":"Metallurgy","score":0.1580287516117096}],"concepts":[{"id":"https://openalex.org/C535196362","wikidata":"https://www.wikidata.org/wiki/Q758","display_name":"Zinc","level":2,"score":0.7008594274520874},{"id":"https://openalex.org/C155672457","wikidata":"https://www.wikidata.org/wiki/Q61231","display_name":"Nanoparticle","level":2,"score":0.6687725186347961},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43982982635498047},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3414519131183624},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3357982635498047},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.2865830063819885},{"id":"https://openalex.org/C171250308","wikidata":"https://www.wikidata.org/wiki/Q11468","display_name":"Nanotechnology","level":1,"score":0.2814505398273468},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.1580287516117096}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/make7030091","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7030091","pdf_url":"https://www.mdpi.com/2504-4990/7/3/91/pdf?version=1756464529","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:fb0b648fd04740f7a803d75f846d653b","is_oa":true,"landing_page_url":"https://doaj.org/article/fb0b648fd04740f7a803d75f846d653b","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":"Machine Learning and Knowledge Extraction, Vol 7, Iss 3, p 91 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/make7030091","is_oa":true,"landing_page_url":"https://doi.org/10.3390/make7030091","pdf_url":"https://www.mdpi.com/2504-4990/7/3/91/pdf?version=1756464529","source":{"id":"https://openalex.org/S4210213891","display_name":"Machine Learning and Knowledge Extraction","issn_l":"2504-4990","issn":["2504-4990"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Learning and Knowledge Extraction","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4413825730.pdf","grobid_xml":"https://content.openalex.org/works/W4413825730.grobid-xml"},"referenced_works_count":39,"referenced_works":["https://openalex.org/W1831301545","https://openalex.org/W2026576354","https://openalex.org/W2069907852","https://openalex.org/W2808327747","https://openalex.org/W2810972983","https://openalex.org/W2898612438","https://openalex.org/W2900716803","https://openalex.org/W3002089204","https://openalex.org/W3039289988","https://openalex.org/W3080297792","https://openalex.org/W3206691116","https://openalex.org/W3212352363","https://openalex.org/W4280589507","https://openalex.org/W4288296172","https://openalex.org/W4292513069","https://openalex.org/W4295765133","https://openalex.org/W4296322994","https://openalex.org/W4316369016","https://openalex.org/W4378676703","https://openalex.org/W4382516982","https://openalex.org/W4385667847","https://openalex.org/W4387452538","https://openalex.org/W4387711801","https://openalex.org/W4389003939","https://openalex.org/W4389352777","https://openalex.org/W4389785756","https://openalex.org/W4390150889","https://openalex.org/W4391652326","https://openalex.org/W4393083123","https://openalex.org/W4393154063","https://openalex.org/W4393339836","https://openalex.org/W4398232658","https://openalex.org/W4400217332","https://openalex.org/W4400585162","https://openalex.org/W4401100781","https://openalex.org/W4402230126","https://openalex.org/W4402572652","https://openalex.org/W4402970431","https://openalex.org/W4404887048"],"related_works":["https://openalex.org/W2946928258","https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474"],"abstract_inverted_index":{"Nanoparticles":[0],"have":[1],"found":[2],"widespread":[3],"application":[4,249],"across":[5,158],"diverse":[6],"fields,":[7],"including":[8],"agriculture":[9],"and":[10,34,73,97,138,150,175,189,209,246],"animal":[11,253],"husbandry.":[12],"However,":[13],"a":[14,41,66,74,86,144,159,230],"persistent":[15],"challenge":[16],"in":[17,53,58,178,238,252],"laboratory-based":[18],"studies":[19],"involving":[20],"nanoparticle":[21,181],"exposure":[22],"is":[23],"the":[24,32,45,102,122,126,169,184,217,244],"limited":[25,105],"availability":[26],"of":[27,36,44,47,71,80,88,104,125,164,171,186,206,212,222,250],"experimental":[28,240],"data,":[29],"which":[30],"constrains":[31],"robustness":[33],"generalizability":[35],"findings.":[37],"This":[38],"study":[39],"presents":[40],"comprehensive":[42],"analysis":[43],"impact":[46],"zinc":[48],"oxide":[49],"nanoparticles":[50,251],"(ZnO":[51],"NPs)":[52],"feed":[54],"on":[55,133,180],"elemental":[56],"homeostasis":[57],"male":[59],"Wistar":[60],"rats.":[61],"Using":[62],"correlation-based":[63],"network":[64],"analysis,":[65],"correlation":[67],"graph":[68],"weight":[69],"value":[70],"15.44":[72],"newly":[75],"proposed":[76],"weighted":[77],"importance":[78],"score":[79],"1.319":[81],"were":[82,148],"calculated,":[83],"indicating":[84],"that":[85],"dose":[87],"3.1":[89],"mg/kg":[90],"represents":[91],"an":[92],"optimal":[93],"balance":[94],"between":[95],"efficacy":[96],"physiological":[98],"stability.":[99],"To":[100],"address":[101],"issue":[103],"sample":[106],"size,":[107],"synthetic":[108,213,226],"data":[109,118,214,227],"generation":[110],"was":[111],"performed":[112],"using":[113],"generative":[114],"adversarial":[115],"networks,":[116],"enabling":[117],"augmentation":[119],"while":[120],"preserving":[121],"statistical":[123],"characteristics":[124],"original":[127],"dataset.":[128],"Machine":[129],"learning":[130,224],"models":[131,153],"based":[132],"fully":[134],"connected":[135],"neural":[136],"networks":[137],"kernel":[139],"ridge":[140],"regression,":[141],"enhanced":[142],"with":[143,225],"custom":[145],"loss":[146],"function,":[147],"developed":[149],"evaluated.":[151],"These":[152],"demonstrated":[154],"strong":[155],"predictive":[156],"performance":[157],"ZnO":[160],"NP":[161],"concentration":[162],"range":[163],"1\u2013150":[165],"mg/kg,":[166],"accurately":[167],"capturing":[168],"dependencies":[170],"essential":[172],"element,":[173],"protein,":[174],"enzyme":[176],"levels":[177],"blood":[179],"dosage.":[182],"Notably,":[183],"presence":[185],"toxic":[187],"elements":[188,192],"some":[190],"other":[191],"at":[193],"ultra-low":[194],"concentrations":[195],"exhibited":[196],"non-random":[197],"patterns,":[198],"suggesting":[199],"potential":[200],"systemic":[201],"responses":[202,237],"or":[203],"early":[204],"indicators":[205],"nanoparticle-induced":[207],"perturbations":[208],"probable":[210],"inability":[211],"to":[215,243],"capture":[216],"true":[218],"dynamics.":[219],"The":[220],"integration":[221],"machine":[223],"expansion":[228],"provides":[229],"promising":[231],"approach":[232],"for":[233],"analyzing":[234],"complex":[235],"biological":[236],"data-scarce":[239],"settings,":[241],"contributing":[242],"safer":[245],"more":[247],"effective":[248],"nutrition.":[254]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}