{"id":"https://openalex.org/W4391569552","doi":"https://doi.org/10.1186/s12859-024-05684-y","title":"A comparison of embedding aggregation strategies in drug\u2013target interaction prediction","display_name":"A comparison of embedding aggregation strategies in drug\u2013target interaction prediction","publication_year":2024,"publication_date":"2024-02-06","ids":{"openalex":"https://openalex.org/W4391569552","doi":"https://doi.org/10.1186/s12859-024-05684-y","pmid":"https://pubmed.ncbi.nlm.nih.gov/38321386"},"language":"en","primary_location":{"id":"doi:10.1186/s12859-024-05684-y","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s12859-024-05684-y","pdf_url":"https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-024-05684-y","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://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-024-05684-y","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5053451093","display_name":"Dimitrios Iliadis","orcid":"https://orcid.org/0000-0002-3676-5940"},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]}],"countries":["BE"],"is_corresponding":true,"raw_author_name":"Dimitrios Iliadis","raw_affiliation_strings":["Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000, Ghent, Belgium. dimitrios.iliadis@ugent.be","Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000, Ghent, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000, Ghent, Belgium. dimitrios.iliadis@ugent.be","institution_ids":["https://openalex.org/I32597200"]},{"raw_affiliation_string":"Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000, Ghent, Belgium","institution_ids":["https://openalex.org/I32597200"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012437460","display_name":"Bernard De Baets","orcid":"https://orcid.org/0000-0002-3876-620X"},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Bernard De Baets","raw_affiliation_strings":["Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000, Ghent, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000, Ghent, Belgium","institution_ids":["https://openalex.org/I32597200"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021993986","display_name":"Tapio Pahikkala","orcid":"https://orcid.org/0000-0003-4183-2455"},"institutions":[{"id":"https://openalex.org/I155660961","display_name":"University of Turku","ror":"https://ror.org/05vghhr25","country_code":"FI","type":"education","lineage":["https://openalex.org/I155660961"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Tapio Pahikkala","raw_affiliation_strings":["Department of Computing, University of Turku, 20500, Turku, Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computing, University of Turku, 20500, Turku, Finland","institution_ids":["https://openalex.org/I155660961"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028945060","display_name":"Willem Waegeman","orcid":"https://orcid.org/0000-0002-5950-3003"},"institutions":[{"id":"https://openalex.org/I32597200","display_name":"Ghent University","ror":"https://ror.org/00cv9y106","country_code":"BE","type":"education","lineage":["https://openalex.org/I32597200"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Willem Waegeman","raw_affiliation_strings":["Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000, Ghent, Belgium"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000, Ghent, Belgium","institution_ids":["https://openalex.org/I32597200"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5053451093"],"corresponding_institution_ids":["https://openalex.org/I32597200"],"apc_list":{"value":2790,"currency":"USD","value_usd":2790},"apc_paid":{"value":2790,"currency":"USD","value_usd":2790},"fwci":2.3073,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.88716754,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"25","issue":"1","first_page":"59","last_page":"59"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":1.0,"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":1.0,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.9973000288009644,"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"}},{"id":"https://openalex.org/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.9858999848365784,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7939109802246094},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6226146221160889},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5917001962661743},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.5584304332733154},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5226656794548035},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5164015889167786},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.4538409411907196},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.45131823420524597},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4327560067176819},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.37014245986938477},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2341950535774231}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7939109802246094},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6226146221160889},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5917001962661743},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.5584304332733154},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5226656794548035},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5164015889167786},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4538409411907196},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.45131823420524597},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4327560067176819},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.37014245986938477},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2341950535774231},{"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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[{"descriptor_ui":"D011211","descriptor_name":"Electric Power Supplies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011211","descriptor_name":"Electric Power Supplies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011211","descriptor_name":"Electric Power Supplies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D011211","descriptor_name":"Electric Power Supplies","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D019985","descriptor_name":"Benchmarking","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D019985","descriptor_name":"Benchmarking","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D055808","descriptor_name":"Drug Discovery","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D055808","descriptor_name":"Drug Discovery","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D055808","descriptor_name":"Drug Discovery","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D055808","descriptor_name":"Drug Discovery","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":6,"locations":[{"id":"doi:10.1186/s12859-024-05684-y","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s12859-024-05684-y","pdf_url":"https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-024-05684-y","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:38321386","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/38321386","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:archive.ugent.be:01HPHAA7S1TPV9MWAEZSAW2BJM","is_oa":true,"landing_page_url":"http://hdl.handle.net/1854/LU-01HPHAA7S1TPV9MWAEZSAW2BJM","pdf_url":"https://biblio.ugent.be/publication/01HPHAA7S1TPV9MWAEZSAW2BJM/file/01HPHAP8KXXE3XJ3EXA9167ZDG.pdf","source":{"id":"https://openalex.org/S4306400478","display_name":"Ghent University Academic Bibliography (Ghent University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I32597200","host_organization_name":"Ghent University","host_organization_lineage":["https://openalex.org/I32597200"],"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":"ISSN: 1471-2105","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:pubmedcentral.nih.gov:10845509","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10845509","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10845509/pdf/12859_2024_Article_5684.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:1895d8144eab4277961995f33da5682e","is_oa":false,"landing_page_url":"https://doaj.org/article/1895d8144eab4277961995f33da5682e","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":"BMC Bioinformatics, Vol 25, Iss 1, Pp 1-18 (2024)","raw_type":"article"},{"id":"pmh:oai:www.utupub.fi:10024/187006","is_oa":true,"landing_page_url":"https://www.utupub.fi/handle/10024/187006","pdf_url":null,"source":{"id":"https://openalex.org/S4306402470","display_name":"UTUPub (University of Turku)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I155660961","host_organization_name":"University of Turku","host_organization_lineage":["https://openalex.org/I155660961"],"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":null,"raw_type":null}],"best_oa_location":{"id":"doi:10.1186/s12859-024-05684-y","is_oa":true,"landing_page_url":"https://doi.org/10.1186/s12859-024-05684-y","pdf_url":"https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-024-05684-y","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":[{"id":"https://metadata.un.org/sdg/3","score":0.4000000059604645,"display_name":"Good health and well-being"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320327336","display_name":"Vlaamse regering","ror":null}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4391569552.pdf"},"referenced_works_count":62,"referenced_works":["https://openalex.org/W174941419","https://openalex.org/W176685087","https://openalex.org/W1466286227","https://openalex.org/W1988037271","https://openalex.org/W2012103676","https://openalex.org/W2029348196","https://openalex.org/W2029538739","https://openalex.org/W2030811942","https://openalex.org/W2035585923","https://openalex.org/W2069293737","https://openalex.org/W2086286404","https://openalex.org/W2103496339","https://openalex.org/W2106029302","https://openalex.org/W2109991441","https://openalex.org/W2117893855","https://openalex.org/W2135007932","https://openalex.org/W2158581396","https://openalex.org/W2253995343","https://openalex.org/W2256119113","https://openalex.org/W2343107734","https://openalex.org/W2605350416","https://openalex.org/W2618530766","https://openalex.org/W2740409734","https://openalex.org/W2768094704","https://openalex.org/W2785947426","https://openalex.org/W2786672974","https://openalex.org/W2788728386","https://openalex.org/W2800011138","https://openalex.org/W2803669957","https://openalex.org/W2860192827","https://openalex.org/W2891165828","https://openalex.org/W2899788782","https://openalex.org/W2900880316","https://openalex.org/W2963091287","https://openalex.org/W2963626582","https://openalex.org/W2967158012","https://openalex.org/W2967481075","https://openalex.org/W2972801466","https://openalex.org/W2978484973","https://openalex.org/W2998496395","https://openalex.org/W3023045848","https://openalex.org/W3032123378","https://openalex.org/W3088777230","https://openalex.org/W3093397714","https://openalex.org/W3096561213","https://openalex.org/W3109916301","https://openalex.org/W3124675547","https://openalex.org/W3125537303","https://openalex.org/W3127077556","https://openalex.org/W3129020707","https://openalex.org/W3166613829","https://openalex.org/W3175599101","https://openalex.org/W4200599555","https://openalex.org/W4292265045","https://openalex.org/W4295298624","https://openalex.org/W4301312111","https://openalex.org/W4317829594","https://openalex.org/W4317951595","https://openalex.org/W4378782196","https://openalex.org/W4385330534","https://openalex.org/W6600474606","https://openalex.org/W6736685754"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W2745001401","https://openalex.org/W4321353415","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W972276598","https://openalex.org/W2087343574","https://openalex.org/W4246352526","https://openalex.org/W2121910908"],"abstract_inverted_index":{"The":[0,89],"prediction":[1],"of":[2,18,38,53,56,81,101,107,123,132,163],"interactions":[3],"between":[4],"novel":[5],"drugs":[6],"and":[7,59,69,142],"biological":[8],"targets":[9],"is":[10,48,111],"a":[11,35],"vital":[12],"step":[13],"in":[14,98,129],"the":[15,19,31,41,51,73,78,82,87,99,105,121,130,154,161,174],"early":[16],"stage":[17],"drug":[20],"discovery":[21],"pipeline.":[22],"Many":[23],"deep":[24],"learning":[25],"approaches":[26],"have":[27],"been":[28,95],"proposed":[29],"over":[30],"last":[32],"decade,":[33],"with":[34],"substantial":[36],"fraction":[37],"them":[39],"sharing":[40],"same":[42,90],"underlying":[43],"two-branch":[44],"architecture.":[45],"Their":[46],"distinction":[47],"limited":[49],"to":[50,76],"use":[52],"different":[54,125,155],"types":[55],"feature":[57],"representations":[58],"branches":[60,83],"(multi-layer":[61],"perceptrons,":[62],"convolutional":[63],"neural":[64,67],"networks,":[65],"graph":[66],"networks":[68],"transformers).":[70],"In":[71,116],"contrast,":[72],"strategy":[74,110],"used":[75,96],"combine":[77],"outputs":[79],"(embeddings)":[80],"has":[84,93],"remained":[85],"mostly":[86],"same.":[88],"general":[91],"architecture":[92],"also":[94],"extensively":[97],"area":[100,131,162],"recommender":[102],"systems,":[103],"where":[104],"choice":[106],"an":[108,113],"aggregation":[109,127],"still":[112],"open":[114],"question.":[115],"this":[117],"work,":[118],"we":[119],"investigate":[120],"effectiveness":[122],"three":[124],"embedding":[126],"strategies":[128,141,156,171],"drug-target":[133],"interaction":[134],"(DTI)":[135],"prediction.":[136],"We":[137,148],"formally":[138],"define":[139],"these":[140],"prove":[143],"their":[144],"universal":[145],"approximator":[146],"capabilities.":[147],"then":[149],"present":[150],"experiments":[151],"that":[152],"compare":[153],"on":[157],"benchmark":[158],"datasets":[159],"from":[160],"DTI":[164],"prediction,":[165],"showcasing":[166],"conditions":[167],"under":[168],"which":[169],"specific":[170],"could":[172],"be":[173],"obvious":[175],"choice.":[176]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":4}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}
