{"id":"https://openalex.org/W4403814890","doi":"https://doi.org/10.48550/arxiv.2409.19991","title":"Robust Multi-view Co-expression Network Inference","display_name":"Robust Multi-view Co-expression Network Inference","publication_year":2024,"publication_date":"2024-09-30","ids":{"openalex":"https://openalex.org/W4403814890","doi":"https://doi.org/10.48550/arxiv.2409.19991"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2409.19991","pdf_url":"http://arxiv.org/pdf/2409.19991","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://arxiv.org/pdf/2409.19991","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5038743843","display_name":"Teodora Pandeva","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pandeva, Teodora","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112104130","display_name":"Martijs J. Jonker","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jonker, Martijs","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066518854","display_name":"Leendert W. Hamoen","orcid":"https://orcid.org/0000-0001-9251-1403"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hamoen, Leendert","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007405944","display_name":"Joris M. Mooij","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mooij, Joris","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5005753034","display_name":"Patrick Forr\u00e9","orcid":"https://orcid.org/0000-0003-4663-3842"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Forr\u00e9, Patrick","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":80},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T13731","display_name":"Advanced Computing and Algorithms","score":0.957,"subfield":{"id":"https://openalex.org/subfields/3322","display_name":"Urban Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T13731","display_name":"Advanced Computing and Algorithms","score":0.957,"subfield":{"id":"https://openalex.org/subfields/3322","display_name":"Urban Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9404,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9166,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/expression","display_name":"Expression (computer science)","score":0.56119907}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.66797584},{"id":"https://openalex.org/C90559484","wikidata":"https://www.wikidata.org/wiki/Q778379","display_name":"Expression (computer science)","level":2,"score":0.56119907},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.52551985},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3491393},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.078772455}],"mesh":[],"locations_count":1,"locations":[{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2409.19991","pdf_url":"http://arxiv.org/pdf/2409.19991","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2409.19991","pdf_url":"http://arxiv.org/pdf/2409.19991","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4396701345","https://openalex.org/W4391913857","https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2381031055","https://openalex.org/W2376932109","https://openalex.org/W2366070815","https://openalex.org/W2358668433"],"abstract_inverted_index":{"Unraveling":[0],"the":[1,8,54,92,128],"co-expression":[2,15,93],"of":[3,10,65],"genes":[4],"across":[5,81],"studies":[6],"enhances":[7],"understanding":[9],"cellular":[11],"processes.":[12],"Inferring":[13],"gene":[14,25,66,118],"networks":[16],"from":[17,45],"transcriptome":[18],"data":[19,120],"presents":[20],"many":[21],"challenges,":[22],"including":[23],"spurious":[24],"correlations,":[26,28],"sample":[27],"and":[29,117],"batch":[30],"effects.":[31],"To":[32],"address":[33],"these":[34],"complexities,":[35],"we":[36,89],"introduce":[37],"a":[38,61,70,74,97],"robust":[39],"method":[40,105],"for":[41,110],"high-dimensional":[42],"graph":[43,130],"inference":[44],"multiple":[46],"independent":[47],"studies.":[48,82],"We":[49],"base":[50],"our":[51,122],"approach":[52],"on":[53,115],"premise":[55],"that":[56,68,88],"each":[57],"dataset":[58],"is":[59,79],"essentially":[60],"noisy":[62],"linear":[63],"mixture":[64],"loadings":[67],"follow":[69],"multivariate":[71],"$t$-distribution":[72],"with":[73],"sparse":[75],"precision":[76],"matrix,":[77],"which":[78],"shared":[80],"This":[83],"allows":[84],"us":[85],"to":[86,96,126,133],"show":[87],"can":[90],"identify":[91],"matrix":[94],"up":[95],"scaling":[98],"factor":[99],"among":[100],"other":[101],"model":[102],"parameters.":[103],"Our":[104],"employs":[106],"an":[107],"Expectation-Maximization":[108],"procedure":[109],"parameter":[111],"estimation.":[112],"Empirical":[113],"evaluation":[114],"synthetic":[116],"expression":[119],"demonstrates":[121],"method's":[123],"improved":[124],"ability":[125],"learn":[127],"underlying":[129],"structure":[131],"compared":[132],"baseline":[134],"methods.":[135]},"abstract_inverted_index_v3":null,"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4403814890","counts_by_year":[],"updated_date":"2025-02-26T09:58:46.525428","created_date":"2024-10-28"}