{"id":"https://openalex.org/W7114777732","doi":"https://doi.org/10.1007/s44163-025-00621-1","title":"Multimodal deep clustering of biobehavioral and environmental factors in children\u2019s social skill development using a self-attentive adversarial network","display_name":"Multimodal deep clustering of biobehavioral and environmental factors in children\u2019s social skill development using a self-attentive adversarial network","publication_year":2025,"publication_date":"2025-12-11","ids":{"openalex":"https://openalex.org/W7114777732","doi":"https://doi.org/10.1007/s44163-025-00621-1"},"language":"en","primary_location":{"id":"doi:10.1007/s44163-025-00621-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-025-00621-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44163-025-00621-1.pdf","source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://link.springer.com/content/pdf/10.1007/s44163-025-00621-1.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Sisi Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210121573","display_name":"Wuchang University of Technology","ror":"https://ror.org/02mqsna37","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210121573"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Sisi Zhao","raw_affiliation_strings":["International Education College, Wuchang Institute of Technology, Wuhan, 430065, Hubei, China"],"affiliations":[{"raw_affiliation_string":"International Education College, Wuchang Institute of Technology, Wuhan, 430065, Hubei, China","institution_ids":["https://openalex.org/I4210121573"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210121573"],"apc_list":{"value":990,"currency":"EUR","value_usd":1067},"apc_paid":{"value":990,"currency":"EUR","value_usd":1067},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.68401335,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"5","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.3174999952316284,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.3174999952316284,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10106","display_name":"Autism Spectrum Disorder Research","score":0.19869999587535858,"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/T11060","display_name":"Infant Development and Preterm Care","score":0.03799999877810478,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child Health"},"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/cluster-analysis","display_name":"Cluster analysis","score":0.7024999856948853},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5841000080108643},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.5640000104904175},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5629000067710876},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4401000142097473},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.3984000086784363},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.3799000084400177}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7024999856948853},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.689300000667572},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6162999868392944},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.602400004863739},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5841000080108643},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5640000104904175},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5629000067710876},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4401000142097473},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.3984000086784363},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3799000084400177},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.3637999892234802},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2888000011444092},{"id":"https://openalex.org/C79416737","wikidata":"https://www.wikidata.org/wiki/Q2305519","display_name":"Social learning","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.26820001006126404},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.2599000036716461},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.25360000133514404},{"id":"https://openalex.org/C132758656","wikidata":"https://www.wikidata.org/wiki/Q5307365","display_name":"Dreyfus model of skill acquisition","level":2,"score":0.25220000743865967},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2500999867916107}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s44163-025-00621-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-025-00621-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44163-025-00621-1.pdf","source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:e86e1af4c55c4c1988477a24898a0ca0","is_oa":true,"landing_page_url":"https://doaj.org/article/e86e1af4c55c4c1988477a24898a0ca0","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Discover Artificial Intelligence, Vol 5, Iss 1, Pp 1-16 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s44163-025-00621-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-025-00621-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44163-025-00621-1.pdf","source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7114777732.pdf","grobid_xml":"https://content.openalex.org/works/W7114777732.grobid-xml"},"referenced_works_count":18,"referenced_works":["https://openalex.org/W3046009294","https://openalex.org/W3133969301","https://openalex.org/W3154940446","https://openalex.org/W3159169960","https://openalex.org/W3173878433","https://openalex.org/W3177080477","https://openalex.org/W4206187495","https://openalex.org/W4214623854","https://openalex.org/W4283376256","https://openalex.org/W4286016535","https://openalex.org/W4308767240","https://openalex.org/W4313535039","https://openalex.org/W4320482536","https://openalex.org/W4321497154","https://openalex.org/W4323305114","https://openalex.org/W4376132353","https://openalex.org/W4385725464","https://openalex.org/W4387666878"],"related_works":[],"abstract_inverted_index":{"This":[0],"study":[1],"proposes":[2],"a":[3],"multimodal":[4,185],"deep":[5,138],"learning":[6],"framework":[7],"for":[8,143,197],"modeling":[9],"the":[10,24,92,119,127,171,177,193],"interplay":[11],"between":[12],"biological,":[13,187],"biomechanical,":[14],"and":[15,49,70,84,95,102,112,121,133,150,165,174,188],"environmental":[16,189],"factors":[17],"in":[18],"children\u2019s":[19,198],"social":[20,147,199],"skill":[21,200],"development.":[22,201],"Using":[23],"SAADSC":[25],"(Self-Attentive":[26],"Adversarial":[27],"Deep":[28],"Subspace":[29],"Clustering)":[30],"model,":[31],"we":[32,181],"integrate":[33],"neurobiological":[34],"data":[35,87,135,190],"(e.g.,":[36,44,53],"cortisol":[37],"levels,":[38],"heart":[39],"rate":[40],"variability),":[41],"biomechanical":[42],"indicators":[43],"postural":[45],"control,":[46],"gait":[47],"symmetry),":[48],"family":[50],"function":[51],"variables":[52],"cohesion,":[54],"stress)":[55],"to":[56,73,90,169,191],"uncover":[57],"latent":[58],"clusters":[59],"reflecting":[60],"developmental":[61,148],"profiles.":[62],"The":[63,124],"model":[64,108],"leverages":[65],"self-attention":[66],"mechanisms,":[67],"adversarial":[68],"training,":[69],"residual":[71],"modules":[72],"extract":[74],"meaningful":[75],"representations":[76],"from":[77,118],"high-dimensional,":[78],"heterogeneous":[79],"data.":[80],"Five":[81],"benchmark":[82,156],"datasets":[83,157],"real-world":[85,184],"social-behavioral":[86],"were":[88,167],"used":[89],"validate":[91,170],"model\u2019s":[93,194],"accuracy":[94],"robustness,":[96],"with":[97,115],"performance":[98],"evaluated":[99],"via":[100],"ACC":[101],"NMI.":[103],"Results":[104],"show":[105],"that":[106],"our":[107],"outperforms":[109],"traditional":[110],"clustering":[111,179],"GAN-based":[113],"baselines,":[114],"notable":[116],"gains":[117],"self-representation":[120],"attention":[122],"modules.":[123],"findings":[125],"support":[126],"feasibility":[128],"of":[129,146,176],"integrating":[130],"neurophysiological,":[131],"behavioral,":[132,186],"contextual":[134],"through":[136],"explainable":[137],"clustering,":[139],"offering":[140],"novel":[141],"insights":[142],"early":[144],"identification":[145],"risk":[149],"personalized":[151],"intervention":[152],"design.":[153],"While":[154],"standard":[155],"such":[158],"as":[159],"MNIST,":[160],"Fashion-MNIST,":[161],"Yale":[162],"B,":[163],"COIL-20,":[164],"USPS":[166],"employed":[168],"technical":[172],"stability":[173],"robustness":[175],"proposed":[178],"framework,":[180],"also":[182],"integrated":[183],"demonstrate":[192],"practical":[195],"relevance":[196]},"counts_by_year":[],"updated_date":"2026-03-18T14:38:29.013473","created_date":"2025-12-11T00:00:00"}