{"id":"https://openalex.org/W7204199476","doi":"https://doi.org/10.1093/bioinformatics/btag585","title":"Comparison of classic statistical methods and machine learning approaches to classify readiness","display_name":"Comparison of classic statistical methods and machine learning approaches to classify readiness","publication_year":2026,"publication_date":"2026-08-25","ids":{"openalex":"https://openalex.org/W7204199476","doi":"https://doi.org/10.1093/bioinformatics/btag585","pmid":"https://pubmed.ncbi.nlm.nih.gov/42640452"},"language":"en","primary_location":{"id":"doi:10.1093/bioinformatics/btag585","doi":"https://doi.org/10.1093/bioinformatics/btag585","is_oa":true,"landing_page_url":"https://doi.org/10.1093/bioinformatics/btag585","pdf_url":"https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btag585/70784326/btag585.pdf","source":{"id":"https://openalex.org/S52395412","display_name":"Bioinformatics","issn_l":"1367-4803","issn":["1367-4803","1367-4811"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["abdc-a","cwts-core","doaj","doyens","jufo-3","ki-jl-2","medline","norway-2"],"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Bioinformatics","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btag585/70784326/btag585.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5044042148","display_name":"Ashley E. Copenhaver","orcid":"https://orcid.org/0009-0004-5564-0192","observed_orcids":["https://orcid.org/0009-0004-5564-0192"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ashley E Copenhaver","raw_affiliation_strings":["Netrias, LLC , Annapolis, MD 21401,"],"raw_orcid":"https://orcid.org/0009-0004-5564-0192","affiliations":[{"raw_affiliation_string":"Netrias, LLC , Annapolis, MD 21401,","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009771483","display_name":"Christopher Puglisi","orcid":"https://orcid.org/0000-0001-5309-589X","observed_orcids":["https://orcid.org/0000-0001-5309-589X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chris Puglisi","raw_affiliation_strings":["Netrias, LLC , Annapolis, MD 21401,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Netrias, LLC , Annapolis, MD 21401,","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036081460","display_name":"Mohammed Eslami","orcid":"https://orcid.org/0000-0001-5488-1542","observed_orcids":["https://orcid.org/0000-0001-5488-1542"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Mohammed A Eslami","raw_affiliation_strings":["Netrias, LLC , Annapolis, MD 21401,"],"raw_orcid":"https://orcid.org/0000-0001-5488-1542","affiliations":[{"raw_affiliation_string":"Netrias, LLC , Annapolis, MD 21401,","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5036081460"],"corresponding_institution_ids":[],"apc_list":{"value":3989,"currency":"USD","value_usd":3989},"apc_paid":{"value":3989,"currency":"USD","value_usd":3989},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.60944819,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"42","issue":"9","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10836","display_name":"Metabolomics and Mass Spectrometry Studies","score":0.5185999870300293,"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/T10836","display_name":"Metabolomics and Mass Spectrometry Studies","score":0.5185999870300293,"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/T10519","display_name":"Advanced Proteomics Techniques and Applications","score":0.08889999985694885,"subfield":{"id":"https://openalex.org/subfields/1607","display_name":"Spectroscopy"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11373","display_name":"Sleep and Work-Related Fatigue","score":0.061900001019239426,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/military-readiness","display_name":"military preparedness","score":0.6179999709129333},{"id":"https://openalex.org/keywords/warfighters","display_name":"warfighters","score":0.828000009059906},{"id":"https://openalex.org/keywords/salivary-biomarkers","display_name":"salivary biomarkers","score":0.7960000038146973},{"id":"https://openalex.org/keywords/proteomics","display_name":"proteomics","score":0.8429999947547913},{"id":"https://openalex.org/keywords/metabolomics","display_name":"metabolomics","score":1.0},{"id":"https://openalex.org/keywords/multi-omics","display_name":"multi-omics","score":0.9070000052452087},{"id":"https://openalex.org/keywords/machine-learning-classification","display_name":"machine learning classification","score":0.7059999704360962},{"id":"https://openalex.org/keywords/stress-physiology","display_name":"stress physiology","score":0.3919999897480011},{"id":"https://openalex.org/keywords/model-generalization","display_name":"model generalization","score":0.7099999785423279}],"concepts":[{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.8003000020980835},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7494999766349792},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6553999781608582},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6410999894142151},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.5321000218391418},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5184999704360962},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.5175999999046326},{"id":"https://openalex.org/C9354725","wikidata":"https://www.wikidata.org/wiki/Q286017","display_name":"Operationalization","level":2,"score":0.4927000105381012},{"id":"https://openalex.org/C163175372","wikidata":"https://www.wikidata.org/wiki/Q3339222","display_name":"Linear model","level":2,"score":0.3716999888420105},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3384999930858612},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.31839999556541443},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.3154999911785126},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.3057999908924103},{"id":"https://openalex.org/C61641136","wikidata":"https://www.wikidata.org/wiki/Q1107019","display_name":"Cognitive load","level":3,"score":0.30169999599456787},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28519999980926514},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.2833999991416931},{"id":"https://openalex.org/C2986587452","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical analysis","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C134290984","wikidata":"https://www.wikidata.org/wiki/Q5141241","display_name":"Cognitive skill","level":3,"score":0.2563999891281128}],"mesh":[{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000069550","descriptor_name":"Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000098429","descriptor_name":"Classification Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098429","descriptor_name":"Classification Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098429","descriptor_name":"Classification Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008889","descriptor_name":"Military Personnel","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D008889","descriptor_name":"Military Personnel","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D008889","descriptor_name":"Military Personnel","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D015415","descriptor_name":"Biomarkers","qualifier_ui":"Q000378","qualifier_name":"metabolism","is_major_topic":false},{"descriptor_ui":"D015415","descriptor_name":"Biomarkers","qualifier_ui":"Q000378","qualifier_name":"metabolism","is_major_topic":false},{"descriptor_ui":"D015415","descriptor_name":"Biomarkers","qualifier_ui":"Q000378","qualifier_name":"metabolism","is_major_topic":false},{"descriptor_ui":"D040901","descriptor_name":"Proteomics","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D040901","descriptor_name":"Proteomics","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D040901","descriptor_name":"Proteomics","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D055432","descriptor_name":"Metabolomics","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D055432","descriptor_name":"Metabolomics","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D055432","descriptor_name":"Metabolomics","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false}],"locations_count":3,"locations":[{"id":"doi:10.1093/bioinformatics/btag585","doi":"https://doi.org/10.1093/bioinformatics/btag585","is_oa":true,"landing_page_url":"https://doi.org/10.1093/bioinformatics/btag585","pdf_url":"https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btag585/70784326/btag585.pdf","source":{"id":"https://openalex.org/S52395412","display_name":"Bioinformatics","issn_l":"1367-4803","issn":["1367-4803","1367-4811"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["abdc-a","cwts-core","doaj","doyens","jufo-3","ki-jl-2","medline","norway-2"],"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Bioinformatics","raw_type":"journal-article"},{"id":"pmid:42640452","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/42640452","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,"listed_in":[],"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":"Bioinformatics (Oxford, England)","raw_type":"Journal Article"},{"id":"pmh:oai:pubmedcentral.nih.gov:13533562","doi":null,"is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13533562/","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13533562/pdf/btag585.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"Bioinformatics","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1093/bioinformatics/btag585","doi":"https://doi.org/10.1093/bioinformatics/btag585","is_oa":true,"landing_page_url":"https://doi.org/10.1093/bioinformatics/btag585","pdf_url":"https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btag585/70784326/btag585.pdf","source":{"id":"https://openalex.org/S52395412","display_name":"Bioinformatics","issn_l":"1367-4803","issn":["1367-4803","1367-4811"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["abdc-a","cwts-core","doaj","doyens","jufo-3","ki-jl-2","medline","norway-2"],"host_organization":"https://openalex.org/P4310311648","host_organization_name":"Oxford University Press","host_organization_lineage":["https://openalex.org/P4310311648","https://openalex.org/P4310311647"],"host_organization_lineage_names":["Oxford University Press","University of Oxford"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Bioinformatics","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.8048999905586243,"display_name":"Good health and well-being"}],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G3307901997","display_name":null,"funder_award_id":"N660012324041","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"}],"funders":[{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"},{"id":"https://openalex.org/F4320332815","display_name":"Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204199476.pdf","grobid_xml":"https://content.openalex.org/works/W7204199476.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"MOTIVATION:":[0],"Predicting":[1],"physical":[2,99],"and":[3,32,100,130,138,155,170,191,201,220,240,247,260,309,317,339,341],"cognitive":[4,101],"readiness":[5,70,108,169,215],"in":[6,135,159],"warfighters":[7,225],"is":[8,34,110,132],"critical":[9],"for":[10,206,229,244],"mission":[11],"success.":[12],"These":[13],"predictions":[14],"can":[15],"be":[16],"improved":[17],"by":[18,30,124,226],"identifying":[19],"key":[20,58],"biomarkers":[21,59,282],"using":[22,324],"multiple":[23],"omics":[24],"modalities.":[25],"The":[26,328],"MASTR-E":[27],"study":[28],"conducted":[29],"McKetney":[31,64],"colleagues":[33],"one":[35,293],"of":[36,42,60,214,236,256,264],"the":[37,104,136,160,217,274,279],"most":[38],"comprehensive":[39],"multi-omics":[40],"studies":[41],"saliva":[43],"samples":[44,77,91],"collected":[45],"from":[46,103,285],"warfighters,":[47],"which":[48],"also":[49],"applied":[50],"classic":[51],"linear":[52],"statistical":[53],"(CLS)":[54],"techniques":[55],"to":[56,82,96,148,183,315],"discover":[57],"readiness.":[61],"Aligning":[62],"with":[63,270,297],"et":[65],"al.'s":[66],"assumptions,":[67],"we":[68,142],"operationalize":[69],"as":[71,80,186,197,291,333],"a":[72,84,112],"binary":[73],"proxy,":[74],"where":[75],"pre-mission":[76],"are":[78,92,312,331,342],"labeled":[79,93],"\"ready\"":[81],"reflect":[83,97],"rested,":[85],"unstressed":[86],"physiological":[87,116,126],"baseline,":[88],"while":[89],"post-mission":[90],"\"not":[94],"ready\"":[95],"cumulative":[98],"load":[102],"mission.":[105],"As":[106],"such,":[107],"here":[109],"not":[111,189],"direct":[113],"biological":[114],"or":[115,180,188],"construct,":[117],"but":[118,278],"an":[119],"inferred":[120],"state":[121],"likely":[122],"dominated":[123],"stress-related":[125],"changes.":[127],"This":[128],"assumption":[129],"definition":[131],"discussed":[133],"further":[134],"Introduction":[137],"Limitations":[139],"sections.":[140],"Here,":[141],"apply":[143],"machine":[144],"learning":[145],"(ML)":[146],"analyses":[147],"better":[149],"assess":[150,222],"generalizability,":[151],"consider":[152],"hidden":[153],"interactions,":[154],"identify":[156,171],"nonlinear":[157],"patterns":[158],"data.":[161],"We":[162,303],"investigated":[163],"whether":[164],"ML":[165,174,307],"approaches":[166],"could":[167],"predict":[168],"relevant":[172],"biomarkers.":[173,199],"models":[175,233,251,308],"were":[176,195,204],"trained":[177],"on":[178,345],"proteomics-only":[179],"metabolomics-only":[181],"datasets":[182,203],"classify":[184],"participants":[185,320],"ready":[187,190],"important":[192],"model":[193],"features":[194,311],"considered":[196],"putative":[198,281],"Training":[200],"testing":[202],"curated":[205],"two":[207],"objectives:":[208],"(i)":[209],"recognize":[210],"biomolecular":[211],"signatures":[212],"indicative":[213],"within":[216],"same":[218],"donor":[219],"(ii)":[221],"generalizability":[223],"across":[224,319],"withholding":[227],"donors":[228],"testing.":[230],"RESULTS:":[231],"Proteomics-based":[232],"achieved":[234,252],"AUCs":[235],"0.907":[237],"\u00b1":[238,242,258,266],"0.034":[239],"0.860":[241],"0.063":[243],"Objectives":[245],"1":[246,254],"2,":[248],"respectively.":[249],"Metabolomics-based":[250],"Objective":[253,261],"AUC":[255,263],"0.994":[257],"0.007":[259],"2":[262],"0.993":[265],"0.010.":[267],"Comparative":[268],"analysis":[269,329],"existing":[271],"literature":[272],"validates":[273],"model's":[275],"feature":[276],"importances,":[277],"identified":[280,299,310,323],"significantly":[283],"differ":[284],"those":[286,298,322],"discovered":[287],"through":[288,300],"CLS":[289,301,325],"analyses,":[290],"only":[292],"ML-identified":[294],"biomarker":[295],"overlapping":[296],"methods.":[302,326],"show":[304],"that":[305],"these":[306],"more":[313],"robust":[314],"noise":[316],"generalizable":[318],"than":[321],"AVAILABILITY:":[327],"pipelines":[330],"provided":[332],"Jupyter":[334],"notebooks,":[335],"including":[336],"all":[337],"code":[338],"documentation,":[340],"available":[343],"publicly":[344],"GitHub":[346],"at":[347],"{https://github.com/netrias/ReadinessClassification}.":[348]},"counts_by_year":[],"updated_date":"2026-10-06T10:28:23.643775","created_date":"2026-08-26T00:00:00"}
