[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124781-en":3,"doc-seo-124781-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124781,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Identification of integrated proteomics and transcriptomics signature of alcohol-associated liver disease using machine learning","Distinguishing alcohol-associated hepatitis and alcohol-associated cirrhosis remains diagnostically challenging. This study applies machine learning using transcriptomics and proteomics from liver tissue and peripheral blood mononuclear cells (PBMCs), covering AH, AC, and healthy controls. Separate pipelines were built for each omics type, then integrated gene–protein expression models generated combined biomarker panels. Transcriptomic liver models reached 90% nested cross-validation accuracy and 82% in independent validation; proteomic liver models reached 100% and 61%. PBMC integration improved classification, with key gene–protein biomarker matches identified.","UCLA  \nUCLA Previously Published Works  \nTitle  \nIdentification of integrated proteomics and transcriptomics signature of alcohol-associated liver disease using machine learning  \nPermalink  \n[https://escholarship.org/uc/item/5669q8pc](https://escholarship.org/uc/item/5669q8pc)  \nJournal  \nPLOS Digital Health, 3(2)  \nISSN  \n2767-3170  \nAuthors  \nListopad, Stanislav  \nMagnan, Christophe Day, Le Z  \net al.  \nPublication Date  \n2024-02-01  \nDOI  \n10.1371/journal.pdig.0000447  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nPLOS DIGITAL HEALTH  \nOPEN ACCESS  \nCitation: Listopad S, Magnan C, Day LZ, Asghar A, Stolz A, Tayek JA, et al. (2024) Identification of integrated proteomics and transcriptomics signature of alcohol-associated liver disease using machine learning. PLOS Digit Health 3(2):  \ne0000447 . [https://doi.org/10.1371/journal](https://doi.org/10.1371/journal). pdig.0000447  \nEditor: Nicole Yee-Key Li-Jessen, McGill University, CANADA  \nReceived: September 8, 2023  \nAccepted: January 9, 2024  \nPublished: February 9, 2024  \nCopyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.  \nData Availability Statement: The human RNA raw sequencing data in this study requires deposit into the Database of Genotypes and Phenotypes (dbGAP) of the National Center for Biotechnology Information (United States National Library of Medicine) with controlled access. The data will be available through dbGaP ([https://www.ncbi.nlm](https://www.ncbi.nlm). [nih.gov/gap/](nih.gov/gap/)) under accession number:  \nphs003112 .v1 .p1 . The public RNA data used for validation in this study is available in the GEO database under accession number GSE142530  \nRESEARCH ARTICLE  \nIdentification of integrated proteomics and transcriptomics signature of alcoholassociated liver disease using machine learning  \nStanislav Listopad1¤ *, Christophe Magnan1, Le Z. Day2, Aliya Asghar3, Andrew Stolz4, John A. Tayek5, Zhang-Xu Liu4, Jon M. Jacobs2, Timothy R. Morgan3, Trina M. NordenKrichmar1,6 *  \n1 Department of Computer Science, University of California, Irvine, California, United States of America,  \n2 Biological Sciences Division and Environmental and Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, United States of America, 3 Medical and Research Services, VALong Beach Healthcare System, Long Beach, California, United States of America, 4 Division of Gastrointestinal & Liver Diseases, Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America, 5 Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Department of Internal Medicine, David Geffen School of Medicine, University of California Los Angeles, Torrance, California, United States of America, 6 Department of Epidemiology and Biostatistics, University of California, Irvine, California, United States of America  \n¤ Current address: Department of Neuroscience, Scripps Research, La Jolla, California, United States of America  \n* [slistopa@uci.edu](slistopa@uci.edu) (SL); [tnordenk@uci.edu](tnordenk@uci.edu) (TMN-K)  \nAbstract  \nDistinguishing between alcohol-associated hepatitis (AH) and alcohol-associated cirrhosis (AC) remains a diagnostic challenge. In this study, we used machine learning with transcriptomics and proteomics data from liver tissue and peripheral mononuclear blood cells (PBMCs) to classify patients with alcohol-associated liver disease. The conditions in the study were AH, AC, and healthy controls. We processed 98 PBMC RNAseq samples, 55 PBMC proteomic samples, 48 liver RNAseq samples, and 53 liver proteomic samples. First, we built separate classification and f","cbCaip7jDJQ9D64P","https://ap.wps.com/l/cbCaip7jDJQ9D64P","pdf",1397186,1,17,"English","en",105,"# Abstract\n## Data and study design\n## Modeling pipelines\n## Validation and performance\n## Integrated multi-omics biomarker panels","[{\"question\":\"What clinical conditions are classified in this study?\",\"answer\":\"The study distinguishes alcohol-associated hepatitis (AH) and alcohol-associated cirrhosis (AC), using healthy controls as a comparison group.\"},{\"question\":\"Which data types and sample sources were used for machine learning?\",\"answer\":\"It uses transcriptomics and proteomics from liver tissue and peripheral mononuclear blood cells (PBMCs), including RNAseq and proteomic measurements.\"},{\"question\":\"How did transcriptomics and proteomics perform in liver tissue and PBMCs?\",\"answer\":\"In liver tissue, transcriptomic models achieved 90% nested cross-validation accuracy and 82% in independent validation, while proteomic models achieved 100% and 61%, respectively. For PBMCs, transcriptomic and proteomic accuracies were 83% and 89%, and integration improved PBMC classification accuracy but not liver tissue.\"}]","Identification of integrated proteomics and transcriptomics signature of alcohol-associated liver disease using machine learning | PDF",1785894614,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"identification-of-integrated-proteomics-and-transcriptomics-signature-of-alcohol-associated-liver-disease-using-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/identification-of-integrated-proteomics-and-transcriptomics-signature-of-alcohol-associated-liver-disease-using-machine-learning/124781/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What clinical conditions are classified in this study?","Question",{"text":75,"@type":76},"The study distinguishes alcohol-associated hepatitis (AH) and alcohol-associated cirrhosis (AC), using healthy controls as a comparison group.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which data types and sample sources were used for machine learning?",{"text":80,"@type":76},"It uses transcriptomics and proteomics from liver tissue and peripheral mononuclear blood cells (PBMCs), including RNAseq and proteomic measurements.",{"name":82,"@type":73,"acceptedAnswer":83},"How did transcriptomics and proteomics perform in liver tissue and PBMCs?",{"text":84,"@type":76},"In liver tissue, transcriptomic models achieved 90% nested cross-validation accuracy and 82% in independent validation, while proteomic models achieved 100% and 61%, respectively. 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