{"id":"https://openalex.org/W4379875932","doi":"https://doi.org/10.3390/e25060909","title":"Identifying Candidate Gene\u2013Disease Associations via Graph Neural Networks","display_name":"Identifying Candidate Gene\u2013Disease Associations via Graph Neural Networks","publication_year":2023,"publication_date":"2023-06-07","ids":{"openalex":"https://openalex.org/W4379875932","doi":"https://doi.org/10.3390/e25060909","pmid":"https://pubmed.ncbi.nlm.nih.gov/37372253"},"language":"en","primary_location":{"id":"doi:10.3390/e25060909","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e25060909","pdf_url":"https://www.mdpi.com/1099-4300/25/6/909/pdf?version=1686271806","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/1099-4300/25/6/909/pdf?version=1686271806","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024240784","display_name":"Pietro Cinaglia","orcid":"https://orcid.org/0000-0003-2237-6984"},"institutions":[{"id":"https://openalex.org/I36443711","display_name":"Magna Graecia University","ror":"https://ror.org/0530bdk91","country_code":"IT","type":"education","lineage":["https://openalex.org/I36443711"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Pietro Cinaglia","raw_affiliation_strings":["Department of Health Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy"],"affiliations":[{"raw_affiliation_string":"Department of Health Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy","institution_ids":["https://openalex.org/I36443711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004845138","display_name":"Mario Cannataro","orcid":"https://orcid.org/0000-0003-1502-2387"},"institutions":[{"id":"https://openalex.org/I36443711","display_name":"Magna Graecia University","ror":"https://ror.org/0530bdk91","country_code":"IT","type":"education","lineage":["https://openalex.org/I36443711"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Mario Cannataro","raw_affiliation_strings":["Data Analytics Research Center, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy"],"affiliations":[{"raw_affiliation_string":"Data Analytics Research Center, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy","institution_ids":["https://openalex.org/I36443711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5024240784"],"corresponding_institution_ids":["https://openalex.org/I36443711"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2165},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2165},"fwci":5.2467,"has_fulltext":true,"cited_by_count":34,"citation_normalized_percentile":{"value":0.96138096,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"25","issue":"6","first_page":"909","last_page":"909"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T10885","display_name":"Gene expression and cancer classification","score":0.9990000128746033,"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"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9987000226974487,"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/graph","display_name":"Graph","score":0.6230660676956177},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6127713918685913},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5518603324890137},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4623602032661438},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.45141294598579407},{"id":"https://openalex.org/keywords/dot-product","display_name":"Dot product","score":0.44253769516944885},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4416450262069702},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.4273897409439087},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4106893539428711},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34692084789276123},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2622406482696533},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.205634206533432}],"concepts":[{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6230660676956177},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6127713918685913},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5518603324890137},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4623602032661438},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.45141294598579407},{"id":"https://openalex.org/C32900221","wikidata":"https://www.wikidata.org/wiki/Q181365","display_name":"Dot product","level":2,"score":0.44253769516944885},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4416450262069702},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4273897409439087},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4106893539428711},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34692084789276123},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2622406482696533},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.205634206533432},{"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.3390/e25060909","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e25060909","pdf_url":"https://www.mdpi.com/1099-4300/25/6/909/pdf?version=1686271806","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:37372253","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37372253","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:10296901","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10296901","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10296901/pdf/entropy-25-00909.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","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":"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:bef215be3ebf4c0a9efaf310ef557288","is_oa":true,"landing_page_url":"https://doaj.org/article/bef215be3ebf4c0a9efaf310ef557288","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":"Entropy, Vol 25, Iss 6, p 909 (2023)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1099-4300/25/6/909/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/e25060909","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"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; Volume 25; Issue 6; Pages: 909","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/e25060909","is_oa":true,"landing_page_url":"https://doi.org/10.3390/e25060909","pdf_url":"https://www.mdpi.com/1099-4300/25/6/909/pdf?version=1686271806","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":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4379875932.pdf"},"referenced_works_count":51,"referenced_works":["https://openalex.org/W1982763211","https://openalex.org/W2095705004","https://openalex.org/W2097749765","https://openalex.org/W2100428244","https://openalex.org/W2118872025","https://openalex.org/W2126884813","https://openalex.org/W2144086069","https://openalex.org/W2151699048","https://openalex.org/W2152575748","https://openalex.org/W2414451196","https://openalex.org/W2537679995","https://openalex.org/W2608690367","https://openalex.org/W2765950793","https://openalex.org/W2809363952","https://openalex.org/W2888358121","https://openalex.org/W2907492528","https://openalex.org/W2912296996","https://openalex.org/W2912636151","https://openalex.org/W2949744095","https://openalex.org/W2972445761","https://openalex.org/W3019246273","https://openalex.org/W3048692209","https://openalex.org/W3048964647","https://openalex.org/W3103409210","https://openalex.org/W3124523381","https://openalex.org/W3152893301","https://openalex.org/W3162538759","https://openalex.org/W3168997536","https://openalex.org/W3179942648","https://openalex.org/W3209731954","https://openalex.org/W4200042293","https://openalex.org/W4200432712","https://openalex.org/W4205126801","https://openalex.org/W4205134630","https://openalex.org/W4205807993","https://openalex.org/W4206078729","https://openalex.org/W4210257598","https://openalex.org/W4213199136","https://openalex.org/W4281699564","https://openalex.org/W4282827695","https://openalex.org/W4282925381","https://openalex.org/W4283260867","https://openalex.org/W4283803602","https://openalex.org/W4294376371","https://openalex.org/W4295241431","https://openalex.org/W4300773848","https://openalex.org/W4309506674","https://openalex.org/W4313412932","https://openalex.org/W6674330103","https://openalex.org/W6759226055","https://openalex.org/W6966749801"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4313906399","https://openalex.org/W4321487865","https://openalex.org/W2811106690","https://openalex.org/W4239306820","https://openalex.org/W2947043951","https://openalex.org/W2318112981","https://openalex.org/W4312417841","https://openalex.org/W4225147082","https://openalex.org/W2778653980"],"abstract_inverted_index":{"Real-world":[0],"objects":[1],"are":[2],"usually":[3],"defined":[4],"in":[5,129,145],"terms":[6],"of":[7,40,63,74,93,117,126,137,156],"their":[8],"own":[9],"relationships":[10],"or":[11],"connections.":[12],"A":[13],"graph":[14,56,89],"(or":[15],"network)":[16],"naturally":[17],"expresses":[18],"this":[19,47],"model":[20,69],"though":[21],"nodes":[22,31],"and":[23,32,76,79,83,97,142],"edges.":[24],"In":[25,46],"biology,":[26],"depending":[27],"on":[28,54,88,114,172],"what":[29],"the":[30,61,110,146,157,173,177],"edges":[33],"represent,":[34],"we":[35,49],"may":[36],"classify":[37],"several":[38],"types":[39],"networks,":[41],"gene-disease":[42],"associations":[43],"(GDAs)":[44],"included.":[45],"paper,":[48],"presented":[50],"a":[51,55,98,115,124,130,151],"solution":[52],"based":[53,87],"neural":[57],"network":[58,112],"(GNN)":[59],"for":[60,109,139,154,189],"identification":[62],"candidate":[64,162],"GDAs.":[65],"We":[66],"trained":[67],"our":[68,166],"with":[70],"an":[71,135],"initial":[72],"set":[73,116],"well-known":[75],"curated":[77],"inter-":[78],"intra-relationships":[80],"between":[81],"genes":[82],"diseases.":[84],"It":[85],"was":[86,170,186],"convolutions,":[90],"making":[91],"use":[92],"multiple":[94],"convolutional":[95],"layers":[96],"point-wise":[99],"non-linearity":[100],"function":[101],"following":[102],"each":[103,121],"layer.":[104],"The":[105,168],"embeddings":[106],"were":[107],"computed":[108],"input":[111],"built":[113],"GDAs":[118,163],"to":[119],"map":[120],"node":[122],"into":[123,150],"vector":[125],"real":[127,147],"numbers":[128],"multidimensional":[131],"space.":[132],"Results":[133],"showed":[134],"AUC":[136],"95%":[138],"training,":[140],"validation,":[141],"testing,":[143],"that":[144],"case":[148],"translated":[149],"positive":[152],"response":[153],"93%":[155],"Top-15":[158],"(highest":[159],"dot":[160],"product)":[161],"identified":[164],"by":[165,183],"solution.":[167],"experimentation":[169],"conducted":[171],"DisGeNET":[174],"dataset,":[175],"while":[176],"DiseaseGene":[178],"Association":[179],"Miner":[180],"(DG-AssocMiner)":[181],"dataset":[182],"Stanford's":[184],"BioSNAP":[185],"also":[187],"processed":[188],"performance":[190],"evaluation":[191],"only.":[192]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":20},{"year":2023,"cited_by_count":2}],"updated_date":"2026-03-27T14:29:43.386196","created_date":"2025-10-10T00:00:00"}