{"id":"https://openalex.org/W2217096657","doi":"https://doi.org/10.1109/bibm.2015.7359905","title":"Integrating prior biological knowledge and graphical LASSO for network inference","display_name":"Integrating prior biological knowledge and graphical LASSO for network inference","publication_year":2015,"publication_date":"2015-11-01","ids":{"openalex":"https://openalex.org/W2217096657","doi":"https://doi.org/10.1109/bibm.2015.7359905","mag":"2217096657"},"language":"en","primary_location":{"id":"doi:10.1109/bibm.2015.7359905","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm.2015.7359905","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102508019","display_name":"Yiming Zuo","orcid":null},"institutions":[{"id":"https://openalex.org/I184565670","display_name":"Georgetown University","ror":"https://ror.org/05vzafd60","country_code":"US","type":"education","lineage":["https://openalex.org/I184565670"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yiming Zuo","raw_affiliation_strings":["Electrical and Computer Engineering, Georgetown University, Washignton, DC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, Georgetown University, Washignton, DC, USA","institution_ids":["https://openalex.org/I184565670"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076006520","display_name":"Guoqiang Yu","orcid":"https://orcid.org/0000-0002-6743-7413"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guoqiang Yu","raw_affiliation_strings":["Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, USA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025125310","display_name":"Habtom W. Ressom","orcid":"https://orcid.org/0000-0001-9296-2132"},"institutions":[{"id":"https://openalex.org/I184565670","display_name":"Georgetown University","ror":"https://ror.org/05vzafd60","country_code":"US","type":"education","lineage":["https://openalex.org/I184565670"]},{"id":"https://openalex.org/I2799903593","display_name":"Georgetown University Medical Center","ror":"https://ror.org/00hjz7x27","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I2799903593"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Habtom W. Ressom","raw_affiliation_strings":["Lombardi Comprehensive Cancer Center, Georgetown University, Washignton, DC, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Lombardi Comprehensive Cancer Center, Georgetown University, Washignton, DC, USA","institution_ids":["https://openalex.org/I2799903593","https://openalex.org/I184565670"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4518,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.6681297,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1543","last_page":"1547"},"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/T10211","display_name":"Computational Drug Discovery Methods","score":0.9998000264167786,"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/T10621","display_name":"Gene Regulatory Network Analysis","score":0.9969000220298767,"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/graphical-model","display_name":"Graphical model","score":0.7081944346427917},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7044016122817993},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6793519854545593},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5679522752761841},{"id":"https://openalex.org/keywords/biological-network","display_name":"Biological network","score":0.5452703237533569},{"id":"https://openalex.org/keywords/lasso","display_name":"Lasso (programming language)","score":0.5451000928878784},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5435114502906799},{"id":"https://openalex.org/keywords/biological-data","display_name":"Biological data","score":0.47793012857437134},{"id":"https://openalex.org/keywords/gene-regulatory-network","display_name":"Gene regulatory network","score":0.4705767035484314},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.417636513710022},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.41140297055244446},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38527506589889526},{"id":"https://openalex.org/keywords/computational-biology","display_name":"Computational biology","score":0.34268152713775635},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3248278796672821},{"id":"https://openalex.org/keywords/bioinformatics","display_name":"Bioinformatics","score":0.2533189356327057},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.14527004957199097},{"id":"https://openalex.org/keywords/gene","display_name":"Gene","score":0.09777653217315674}],"concepts":[{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.7081944346427917},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7044016122817993},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6793519854545593},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5679522752761841},{"id":"https://openalex.org/C28225019","wikidata":"https://www.wikidata.org/wiki/Q4915005","display_name":"Biological network","level":2,"score":0.5452703237533569},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.5451000928878784},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5435114502906799},{"id":"https://openalex.org/C201797286","wikidata":"https://www.wikidata.org/wiki/Q4914986","display_name":"Biological data","level":2,"score":0.47793012857437134},{"id":"https://openalex.org/C67339327","wikidata":"https://www.wikidata.org/wiki/Q1502576","display_name":"Gene regulatory network","level":4,"score":0.4705767035484314},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.417636513710022},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.41140297055244446},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38527506589889526},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.34268152713775635},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3248278796672821},{"id":"https://openalex.org/C60644358","wikidata":"https://www.wikidata.org/wiki/Q128570","display_name":"Bioinformatics","level":1,"score":0.2533189356327057},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.14527004957199097},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.09777653217315674},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C150194340","wikidata":"https://www.wikidata.org/wiki/Q26972","display_name":"Gene expression","level":3,"score":0.0},{"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/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm.2015.7359905","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm.2015.7359905","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2081195764","https://openalex.org/W2096801725","https://openalex.org/W2097554668","https://openalex.org/W2105760337","https://openalex.org/W2110065044","https://openalex.org/W2112811019","https://openalex.org/W2132555912","https://openalex.org/W2133465414","https://openalex.org/W2154947819","https://openalex.org/W2158217645","https://openalex.org/W2163485494","https://openalex.org/W2165556326","https://openalex.org/W2171030481"],"related_works":["https://openalex.org/W2001490496","https://openalex.org/W2182544397","https://openalex.org/W2616545131","https://openalex.org/W3083599310","https://openalex.org/W1993746015","https://openalex.org/W2962993057","https://openalex.org/W2155492529","https://openalex.org/W4252504573","https://openalex.org/W2142063839","https://openalex.org/W2131550814"],"abstract_inverted_index":{"Systems":[0],"biology":[1],"aims":[2],"at":[3],"unravelling":[4],"the":[5,16,63,127,138,165],"mechanisms":[6],"of":[7,15,148,167],"complex":[8],"diseases":[9],"by":[10,146,181],"investigating":[11],"how":[12],"individual":[13],"elements":[14],"cell":[17],"(e.g.,":[18,133],"genes,":[19],"proteins,":[20],"metabolites,":[21],"etc.)":[22],"interact":[23],"with":[24,157],"each":[25],"other.":[26],"Network-based":[27],"methods":[28],"provide":[29],"an":[30],"intuitive":[31],"framework":[32],"to":[33,61,105,117,164,175],"model,":[34],"characterize,":[35],"and":[36,81,96,108,123,155,170],"understand":[37],"these":[38],"interactions.":[39],"To":[40],"reconstruct":[41],"a":[42,58,74,97,130,142],"biological":[43,88],"network,":[44],"one":[45],"can":[46],"either":[47],"query":[48],"public":[49],"databases":[50],"for":[51],"known":[52],"interactions":[53,92],"(knowledge-driven":[54],"approach)":[55],"or":[56],"build":[57],"mathematical":[59],"model":[60,100],"measure":[62],"associations":[64],"from":[65,93,129,150],"data":[66],"(data-driven":[67],"approach).":[68],"In":[69],"this":[70],"paper,":[71],"we":[72],"propose":[73],"new":[75],"network":[76,113],"inference":[77],"method,":[78],"integrating":[79],"knowledge":[80,89],"data-driven":[82],"approaches.":[83],"The":[84,112,160],"method":[85,140],"integrates":[86],"prior":[87],"(i.e.,":[90,101],"protein-protein":[91],"BioGRID":[94],"database)":[95],"Gaussian":[98],"graphical":[99,102],"LASSO":[103],"algorithm)":[104],"construct":[106],"robust":[107],"biologically":[109],"relevant":[110],"network.":[111],"is":[114],"then":[115],"utilized":[116],"extract":[118],"differential":[119,161],"sub-networks":[120,162],"between":[121],"case":[122],"control":[124],"groups":[125],"using":[126],"result":[128],"statistical":[131],"analysis":[132,147],"logistic":[134],"regression).":[135],"We":[136],"applied":[137],"proposed":[139],"on":[141],"proteomic":[143],"dataset":[144],"acquired":[145],"sera":[149],"hepatocellular":[151],"carcinoma":[152],"(HCC)":[153],"cases":[154],"patients":[156],"liver":[158],"cirrhosis.":[159],"led":[163],"identification":[166],"hub":[168],"proteins":[169],"key":[171],"pathways,":[172],"whose":[173],"relevance":[174],"HCC":[176],"study":[177],"has":[178],"been":[179],"confirmed":[180],"literature":[182],"survey.":[183]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}