{"id":"https://openalex.org/W4286542683","doi":"https://doi.org/10.1109/tnnls.2022.3190321","title":"Survival Analysis of High-Dimensional Data With Graph Convolutional Networks and Geometric Graphs","display_name":"Survival Analysis of High-Dimensional Data With Graph Convolutional Networks and Geometric Graphs","publication_year":2022,"publication_date":"2022-07-21","ids":{"openalex":"https://openalex.org/W4286542683","doi":"https://doi.org/10.1109/tnnls.2022.3190321","pmid":"https://pubmed.ncbi.nlm.nih.gov/35862325"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2022.3190321","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2022.3190321","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://discovery.ucl.ac.uk/10152571/1/YurongLing-TNNLS-GCN-accepted.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042450458","display_name":"Yurong Ling","orcid":"https://orcid.org/0000-0001-6302-4569"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yurong Ling","raw_affiliation_strings":["Department of Statistical Science, University College London, London, U.K"],"raw_orcid":"https://orcid.org/0000-0001-6302-4569","affiliations":[{"raw_affiliation_string":"Department of Statistical Science, University College London, London, U.K","institution_ids":["https://openalex.org/I45129253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101887097","display_name":"Zijing Liu","orcid":"https://orcid.org/0000-0002-0189-7409"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Zijing Liu","raw_affiliation_strings":["Department of Mathematics, Imperial College London, London, U.K"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, Imperial College London, London, U.K","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079361172","display_name":"Jing\u2010Hao Xue","orcid":"https://orcid.org/0000-0003-1174-610X"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Jing-Hao Xue","raw_affiliation_strings":["Department of Statistical Science, University College London, London, U.K"],"raw_orcid":"https://orcid.org/0000-0003-1174-610X","affiliations":[{"raw_affiliation_string":"Department of Statistical Science, University College London, London, U.K","institution_ids":["https://openalex.org/I45129253"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0443,"has_fulltext":true,"cited_by_count":11,"citation_normalized_percentile":{"value":0.79682119,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"35","issue":"4","first_page":"4876","last_page":"4886"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9830999970436096,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9830999970436096,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9764000177383423,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.967199981212616,"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":0.5539549589157104},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.521307647228241},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4221533238887787},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3544928729534149},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3443715274333954},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3209395408630371},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.31990084052085876}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5539549589157104},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.521307647228241},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4221533238887787},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3544928729534149},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3443715274333954},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3209395408630371},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31990084052085876}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tnnls.2022.3190321","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2022.3190321","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:35862325","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35862325","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":"IEEE transactions on neural networks and learning systems","raw_type":null},{"id":"pmh:oai:eprints.ucl.ac.uk.OAI2:10152571","is_oa":true,"landing_page_url":"https://discovery.ucl.ac.uk/id/eprint/10152571/","pdf_url":"https://discovery.ucl.ac.uk/10152571/1/YurongLing-TNNLS-GCN-accepted.pdf","source":{"id":"https://openalex.org/S4306400024","display_name":"UCL Discovery (University College London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I45129253","host_organization_name":"University College London","host_organization_lineage":["https://openalex.org/I45129253"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems pp. 1-11. (2022) (In press).","raw_type":"Article"}],"best_oa_location":{"id":"pmh:oai:eprints.ucl.ac.uk.OAI2:10152571","is_oa":true,"landing_page_url":"https://discovery.ucl.ac.uk/id/eprint/10152571/","pdf_url":"https://discovery.ucl.ac.uk/10152571/1/YurongLing-TNNLS-GCN-accepted.pdf","source":{"id":"https://openalex.org/S4306400024","display_name":"UCL Discovery (University College London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I45129253","host_organization_name":"University College London","host_organization_lineage":["https://openalex.org/I45129253"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems pp. 1-11. (2022) (In press).","raw_type":"Article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.4000000059604645}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4286542683.pdf","grobid_xml":"https://content.openalex.org/works/W4286542683.grobid-xml"},"referenced_works_count":40,"referenced_works":["https://openalex.org/W1965801346","https://openalex.org/W1986546598","https://openalex.org/W1990534247","https://openalex.org/W1991415686","https://openalex.org/W2000703258","https://openalex.org/W2052825782","https://openalex.org/W2057998328","https://openalex.org/W2070216889","https://openalex.org/W2073241381","https://openalex.org/W2108268158","https://openalex.org/W2109242212","https://openalex.org/W2122328291","https://openalex.org/W2129925362","https://openalex.org/W2149199519","https://openalex.org/W2157076315","https://openalex.org/W2158698691","https://openalex.org/W2305849921","https://openalex.org/W2753919178","https://openalex.org/W2789172526","https://openalex.org/W2797883881","https://openalex.org/W2890655214","https://openalex.org/W2907101105","https://openalex.org/W2928673187","https://openalex.org/W2947983671","https://openalex.org/W2948980173","https://openalex.org/W2954512644","https://openalex.org/W2963232127","https://openalex.org/W2963246719","https://openalex.org/W2964015378","https://openalex.org/W2964051675","https://openalex.org/W2964084755","https://openalex.org/W2986832571","https://openalex.org/W3023365278","https://openalex.org/W3094325009","https://openalex.org/W3097349486","https://openalex.org/W3147894994","https://openalex.org/W6726873649","https://openalex.org/W6754929296","https://openalex.org/W6764798969","https://openalex.org/W6770551379"],"related_works":["https://openalex.org/W4205762803","https://openalex.org/W2535856026","https://openalex.org/W2265065644","https://openalex.org/W2134699697","https://openalex.org/W3017188156","https://openalex.org/W2322875716","https://openalex.org/W2383516975","https://openalex.org/W2374878784","https://openalex.org/W2147679489","https://openalex.org/W2371642785"],"abstract_inverted_index":{"This":[0],"article":[1],"proposes":[2],"a":[3,88,103,144],"survival":[4,55,80,91,118,137,156],"model":[5,89,112],"based":[6,93,140],"on":[7,94,130,141],"graph":[8],"convolutional":[9],"networks":[10],"(GCNs)":[11],"with":[12,46,72],"geometric":[13,62,100,152],"graphs":[14,25,42,63,74,101,153],"directly":[15],"constructed":[16],"from":[17,65,83],"high-dimensional":[18,66,159],"features.":[19],"First,":[20],"we":[21,58,86],"clarify":[22],"that":[23,43,60,134,151],"the":[24,35,41,47,110,122,135],"used":[26,76],"in":[27,33,77],"GCNs":[28,78,142],"play":[29],"an":[30],"important":[31],"role":[32],"processing":[34],"relational":[36],"information":[37],"of":[38,125,146,158],"samples,":[39],"and":[40,102,120,149],"align":[44],"well":[45],"underlying":[48],"data":[49,67],"structure":[50],"could":[51],"be":[52],"beneficial":[53],"for":[54,79,90],"analysis.":[56,81],"Second,":[57],"show":[59,133],"sparse":[61,99],"derived":[64],"are":[68],"more":[69],"favorable":[70],"compared":[71],"dense":[73],"when":[75],"Third,":[82],"this":[84],"insight,":[85],"propose":[87],"analysis":[92,119,138,157],"GCNs.":[95],"By":[96],"using":[97],"multiple":[98],"proposed":[104,136],"sequential":[105],"forward":[106],"floating":[107],"selection":[108],"algorithm,":[109],"new":[111],"is":[113],"able":[114],"to":[115],"simultaneously":[116],"perform":[117],"unveil":[121],"local":[123],"neighborhoods":[124],"samples.":[126],"The":[127],"experimental":[128],"results":[129],"real-world":[131],"datasets":[132],"approach":[139],"outperforms":[143],"variety":[145],"existing":[147],"methods":[148],"indicate":[150],"can":[154],"aid":[155],"data.":[160]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-25T15:57:00.446498","created_date":"2025-10-10T00:00:00"}