{"id":"https://openalex.org/W4399955382","doi":"https://doi.org/10.3390/e26070541","title":"Learning Causes of Functional Dynamic Targets: Screening and Local Methods","display_name":"Learning Causes of Functional Dynamic Targets: Screening and Local Methods","publication_year":2024,"publication_date":"2024-06-24","ids":{"openalex":"https://openalex.org/W4399955382","doi":"https://doi.org/10.3390/e26070541","pmid":"https://pubmed.ncbi.nlm.nih.gov/39056904"},"language":"en","primary_location":{"id":"doi:10.3390/e26070541","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e26070541","pdf_url":"https://www.mdpi.com/1099-4300/26/7/541/pdf?version=1719237053","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"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":"Entropy","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1099-4300/26/7/541/pdf?version=1719237053","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036771265","display_name":"Ruiqi Zhao","orcid":"https://orcid.org/0000-0002-6198-052X"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruiqi Zhao","raw_affiliation_strings":["School of Mathematical Sciences, Peking University, Beijing 100871, China"],"raw_orcid":"https://orcid.org/0000-0002-6198-052X","affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences, Peking University, Beijing 100871, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052393396","display_name":"Xiaoxia Yang","orcid":"https://orcid.org/0000-0003-2185-7401"},"institutions":[{"id":"https://openalex.org/I31683504","display_name":"Beijing Forestry University","ror":"https://ror.org/04xv2pc41","country_code":"CN","type":"education","lineage":["https://openalex.org/I1327237609","https://openalex.org/I31683504","https://openalex.org/I4210127390"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaoxia Yang","raw_affiliation_strings":["College of Science, Beijing Forestry University, Beijing 100083, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Science, Beijing Forestry University, Beijing 100083, China","institution_ids":["https://openalex.org/I31683504"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036379641","display_name":"Yangbo He","orcid":"https://orcid.org/0000-0003-1153-588X"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yangbo He","raw_affiliation_strings":["School of Mathematical Sciences, Peking University, Beijing 100871, China"],"raw_orcid":"https://orcid.org/0000-0003-1153-588X","affiliations":[{"raw_affiliation_string":"School of Mathematical Sciences, Peking University, Beijing 100871, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5036379641","https://openalex.org/A5052393396"],"corresponding_institution_ids":["https://openalex.org/I20231570","https://openalex.org/I31683504"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2227},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2227},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.06583101,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"26","issue":"7","first_page":"541","last_page":"541"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9815999865531921,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9815999865531921,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9484999775886536,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9172999858856201,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7002886533737183},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5665169954299927},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5366828441619873},{"id":"https://openalex.org/keywords/basis","display_name":"Basis (linear algebra)","score":0.4426555633544922},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18842896819114685}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7002886533737183},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5665169954299927},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5366828441619873},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.4426555633544922},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18842896819114685},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3390/e26070541","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e26070541","pdf_url":"https://www.mdpi.com/1099-4300/26/7/541/pdf?version=1719237053","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"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":"Entropy","raw_type":"journal-article"},{"id":"pmid:39056904","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/39056904","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":"Entropy (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:11275285","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11275285","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11275285/pdf/entropy-26-00541.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":"Entropy (Basel)","raw_type":"Text"},{"id":"pmh:oai:doaj.org/article:d6b2713f1dde48d7b53117d13da6e67f","is_oa":false,"landing_page_url":"https://doaj.org/article/d6b2713f1dde48d7b53117d13da6e67f","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Entropy, Vol 26, Iss 7, p 541 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/e26070541","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e26070541","pdf_url":"https://www.mdpi.com/1099-4300/26/7/541/pdf?version=1719237053","source":{"id":"https://openalex.org/S195231649","display_name":"Entropy","issn_l":"1099-4300","issn":["1099-4300"],"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":"Entropy","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G870971120","display_name":null,"funder_award_id":"2022ZD0160300","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4399955382.pdf"},"referenced_works_count":31,"referenced_works":["https://openalex.org/W1502621380","https://openalex.org/W1694552428","https://openalex.org/W1822300807","https://openalex.org/W1965507311","https://openalex.org/W1975062332","https://openalex.org/W1989646326","https://openalex.org/W2006898657","https://openalex.org/W2009120796","https://openalex.org/W2013374740","https://openalex.org/W2014887862","https://openalex.org/W2073307618","https://openalex.org/W2082785772","https://openalex.org/W2098038934","https://openalex.org/W2103614491","https://openalex.org/W2111061246","https://openalex.org/W2114123573","https://openalex.org/W2130095137","https://openalex.org/W2143891888","https://openalex.org/W2156571267","https://openalex.org/W2293860982","https://openalex.org/W2295183944","https://openalex.org/W2324228145","https://openalex.org/W2973192395","https://openalex.org/W3020913839","https://openalex.org/W3136547954","https://openalex.org/W6634071051","https://openalex.org/W6637292104","https://openalex.org/W6653832536","https://openalex.org/W6682904970","https://openalex.org/W6687443243","https://openalex.org/W6791831767"],"related_works":["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","https://openalex.org/W4283697347"],"abstract_inverted_index":{"This":[0],"paper":[1],"addresses":[2],"the":[3,29,33,49,52,64,71,89,93,100,116,120,147,162,169,172],"challenge":[4],"of":[5,15,32,51,119,171],"identifying":[6],"causes":[7,31,40,118,132,148,170],"for":[8],"functional":[9,53],"dynamic":[10,54],"targets,":[11],"which":[12],"are":[13,67],"functions":[14],"various":[16],"variables":[17,65,125],"over":[18],"time.":[19],"We":[20,46,138],"develop":[21],"screening":[22,60,82],"and":[23,83,95,126],"local":[24,112],"learning":[25,85,168],"methods":[26,144,164],"to":[27,42,62,87,114,134],"learn":[28,146],"direct":[30,117],"target,":[34],"as":[35,37],"well":[36,166],"all":[38,130],"indirect":[39,131],"up":[41,133],"a":[43,59,111,135],"given":[44,136],"distance.":[45,137],"first":[47],"discuss":[48],"modeling":[50],"target.":[55,72,173],"Then,":[56],"we":[57,76,109],"propose":[58,110],"method":[61,113],"select":[63],"that":[66,80,141,161],"significantly":[68],"correlated":[69],"with":[70],"On":[73],"this":[74],"basis,":[75],"introduce":[77],"an":[78],"algorithm":[79],"combines":[81],"structural":[84],"techniques":[86],"uncover":[88],"causal":[90,105],"structure":[91],"among":[92],"target":[94,121],"its":[96],"causes.":[97],"To":[98],"tackle":[99],"distance":[101],"effect,":[102],"where":[103],"long":[104],"paths":[106],"weaken":[107],"correlation,":[108],"discover":[115],"in":[122,167],"these":[123],"significant":[124],"further":[127],"sequentially":[128],"find":[129],"show":[139,160],"theoretically":[140],"our":[142],"proposed":[143,163],"can":[145],"correctly":[149],"under":[150],"some":[151],"regular":[152],"assumptions.":[153],"Experiments":[154],"based":[155],"on":[156],"synthetic":[157],"data":[158],"also":[159],"perform":[165]},"counts_by_year":[],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}