{"id":"https://openalex.org/W6977382962","doi":"https://doi.org/10.6094/unifr/151256","title":"Netboost: statistical modeling strategies for high-dimensional data","display_name":"Netboost: statistical modeling strategies for high-dimensional data","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W6977382962","doi":"https://doi.org/10.6094/unifr/151256"},"language":"en","primary_location":{"id":"doi:10.6094/unifr/151256","is_oa":true,"landing_page_url":"https://doi.org/10.6094/unifr/151256","pdf_url":null,"source":{"id":"https://openalex.org/S4306401057","display_name":"FreiDok plus (Universit\u00e4tsbibliothek Freiburg)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I161046081","host_organization_name":"University of Freiburg","host_organization_lineage":["https://openalex.org/I161046081"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},"type":"other","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.6094/unifr/151256","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Schlosser, Pascal","orcid":"https://orcid.org/0000-0002-8460-0462"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Schlosser, Pascal","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-8460-0462","affiliations":[]}],"institutions":[],"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":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.6567000150680542},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.6499000191688538},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6236000061035156},{"id":"https://openalex.org/keywords/hierarchical-clustering","display_name":"Hierarchical clustering","score":0.5375000238418579},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.44760000705718994},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.42590001225471497},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.3849000036716461},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.37220001220703125},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.3626999855041504},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.357699990272522}],"concepts":[{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.6567000150680542},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.6499000191688538},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6236000061035156},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5453000068664551},{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.5375000238418579},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.53329998254776},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.44760000705718994},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.42590001225471497},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38909998536109924},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.3849000036716461},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.38449999690055847},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.37220001220703125},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.3626999855041504},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.357699990272522},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3481999933719635},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.33889999985694885},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3359000086784363},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3294000029563904},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.32760000228881836},{"id":"https://openalex.org/C96608239","wikidata":"https://www.wikidata.org/wiki/Q1199823","display_name":"Statistical power","level":2,"score":0.32199999690055847},{"id":"https://openalex.org/C21308566","wikidata":"https://www.wikidata.org/wiki/Q7169365","display_name":"Permutation (music)","level":2,"score":0.32120001316070557},{"id":"https://openalex.org/C201797286","wikidata":"https://www.wikidata.org/wiki/Q4914986","display_name":"Biological data","level":2,"score":0.3001999855041504},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.2865000069141388},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C144986985","wikidata":"https://www.wikidata.org/wiki/Q871236","display_name":"Hierarchical database model","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C168743327","wikidata":"https://www.wikidata.org/wiki/Q1826427","display_name":"Random effects model","level":3,"score":0.275299996137619},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C144559511","wikidata":"https://www.wikidata.org/wiki/Q2986279","display_name":"Principal (computer security)","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C138958017","wikidata":"https://www.wikidata.org/wiki/Q190087","display_name":"Data type","level":2,"score":0.2621999979019165},{"id":"https://openalex.org/C25249476","wikidata":"https://www.wikidata.org/wiki/Q6934678","display_name":"Multifactor dimensionality reduction","level":5,"score":0.2619999945163727},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.2581999897956848},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.2529999911785126}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.6094/unifr/151256","is_oa":true,"landing_page_url":"https://doi.org/10.6094/unifr/151256","pdf_url":null,"source":{"id":"https://openalex.org/S4306401057","display_name":"FreiDok plus (Universit\u00e4tsbibliothek Freiburg)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I161046081","host_organization_name":"University of Freiburg","host_organization_lineage":["https://openalex.org/I161046081"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"doi:10.6094/unifr/151256","is_oa":true,"landing_page_url":"https://doi.org/10.6094/unifr/151256","pdf_url":null,"source":{"id":"https://openalex.org/S4306401057","display_name":"FreiDok plus (Universit\u00e4tsbibliothek Freiburg)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I161046081","host_organization_name":"University of Freiburg","host_organization_lineage":["https://openalex.org/I161046081"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Background:":[0],"State-of-the":[1],"art":[2],"methods":[3],"often":[4],"fail":[5],"to":[6,58,76,129,182,193,223,294,360],"identify":[7,59,77],"weak":[8],"but":[9],"cumulative":[10],"effects":[11,21],"of":[12,33,63,100,114,153,176,209,215,298,308,321,330,363,373],"variables":[13,364],"found":[14,339],"in":[15,25,119,144,171,213,248,254,279,289,305,314,371,411],"high-dimensional":[16,154,391],"omics":[17],"datasets.":[18,256],"Nevertheless,":[19],"these":[20,96,177],"play":[22],"important":[23],"roles":[24],"many":[26,175],"diseases,":[27,150],"such":[28],"as":[29,167,169,325,327,397],"the":[30,54,60,64,73,101,112,115,131,236,242,286,290,296,306,331],"clonal":[31],"development":[32],"leukemic":[34],"cells":[35],"and":[36,79,134,165,197,204,225,336,338,365,409,419],"CKD":[37],"metabolism.":[38],"Results:":[39],"We":[40,110,140],"propose":[41],"Netboost,":[42,334],"a":[43,106,151,260,276,317,354,385,398],"three-step":[44],"dimension":[45],"reduction":[46,404],"technique.":[47],"First,":[48,257],"boosting-based":[49],"filters":[50],"are":[51],"combined":[52],"with":[53,121,191,266,275,350],"topological":[55],"overlap":[56],"measure":[57],"essential":[61],"edges":[62,75],"network.":[65],"Second,":[66,283],"sparse":[67],"hierarchical":[68],"clustering":[69],"is":[70,83,91,358,394],"applied":[71,141],"on":[72,95],"selected":[74],"modules":[78],"finally,":[80],"module":[81],"information":[82],"aggregated":[84],"by":[85,200,235],"principal":[86],"components.":[87],"The":[88,379],"primary":[89],"analysis":[90,189,214,413],"then":[92],"carried":[93],"out":[94],"summary":[97],"measures":[98],"instead":[99],"original":[102],"data,":[103,392],"allowing":[104],"for":[105,123,137,390],"localized":[107],"dimensionality":[108,403],"reduction.":[109],"demonstrate":[111],"application":[113],"newly":[116,380],"developed":[117,381],"Netboost":[118,322,349,355,383],"integration":[120,208],"CoxBoost":[122],"survival":[124,281],"prediction,":[125],"genetic":[126],"association":[127],"studies":[128],"understand":[130],"human":[132,166,291],"metabolism":[133,293],"random":[135],"forests":[136],"disease":[138],"classification.":[139,420],"our":[142,210],"method":[143,212,341],"7":[145],"independent":[146,205,255],"cohorts":[147],"spanning":[148],"6":[149],"variety":[152],"data":[155],"types":[156],"(DNA":[157],"methylation,":[158],"metabolomics,":[159],"miRNA,":[160],"RNA":[161,163],"arrays,":[162],"sequencing)":[164],"well":[168,326],"murine":[170],"vivo":[172],"samples.":[173],"In":[174,240],"settings,":[178,414],"we":[179,220,258,284,312,347],"were":[180,221,251],"able":[181,222],"show":[183],"significant":[184],"advantages":[185],"over":[186],"state-of-the-art":[187,238],"competitive":[188],"strategies":[190],"respect":[192],"prediction":[194],"errors,":[195],"power":[196,408],"mis-classification":[198],"rates":[199],"cross-validation,":[201],"general":[202],"resampling":[203],"replication.":[206],"By":[207],"novel":[211],"several":[216,315],"biomedical":[217],"research":[218],"projects,":[219],"attain":[224],"confirm":[226],"biological":[227],"insights":[228],"which":[229,269,301,357,393],"could":[230],"not":[231],"have":[232],"been":[233],"reached":[234],"compared":[237],"methods.":[239],"particular,":[241],"two":[243,273],"biologically":[244],"most":[245],"insightful":[246],"findings":[247],"this":[249],"dissertation":[250],"both":[252],"replicated":[253],"identified":[259],"chromatin":[261],"modifying":[262],"enzyme":[263],"signature":[264],"associated":[265],"overall":[267,323],"survival,":[268],"separates":[270],"patients":[271],"into":[272],"groups":[274],"threefold":[277],"difference":[278],"median":[280],"time.":[282],"established":[285],"central":[287],"concept":[288],"urinary":[292],"be":[295],"list":[297],"ADME":[299],"processes,":[300],"was":[302],"originally":[303],"defined":[304],"context":[307],"pharmacological":[309],"research.":[310],"Furthermore,":[311],"demonstrated":[313],"datasets":[316],"lower":[318],"sampling":[319,344],"uncertainty":[320,342],"networks":[324,332],"individual":[328],"components":[329],"across":[333],"WGCNA":[335],"k-means":[337],"that":[340],"dominated":[343],"uncertainty.":[345],"Finally,":[346],"integrate":[348],"robust":[351],"methodology":[352],"designing":[353],"adaption,":[356],"invariant":[359],"monotone":[361],"transformations":[362],"thus":[366],"obtain":[367],"an":[368],"advantageous":[369],"extension":[370],"cases":[372],"non-linear":[374],"relationships":[375],"between":[376],"variables.":[377],"Conclusion:":[378],"approach":[382],"offers":[384],"versatile":[386],"statistical":[387],"modeling":[388],"strategy":[389],"freely":[395],"available":[396],"Bioconductor":[399],"R":[400],"package.":[401],"Via":[402],"it":[405],"improves":[406],"accuracy,":[407],"stability":[410],"various":[412],"including":[415],"time-to-event":[416],"analysis,":[417],"GWAS":[418]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}
