{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T12:08:01Z","timestamp":1779365281060,"version":"3.53.0"},"reference-count":58,"publisher":"Elsevier","isbn-type":[{"value":"9780443413490","type":"print"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1016\/bs.pmbts.2026.02.004","type":"book-chapter","created":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T22:36:48Z","timestamp":1775515008000},"page":"241-259","source":"Crossref","is-referenced-by-count":0,"title":["AI in multi-omics analysis of liver diseases"],"prefix":"10.1016","author":[{"given":"Hifzur Rahman","family":"Ansari","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Rehan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Firoz","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/bs.pmbts.2026.02.004_bib1","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1016\/j.jhep.2023.03.017","article-title":"Global burden of liver disease: 2023 update","volume":"79","author":"Devarbhavi","year":"2023","journal-title":"J Hepatol"},{"issue":"1","key":"10.1016\/bs.pmbts.2026.02.004_bib2","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.jhep.2018.09.014","article-title":"Burden of liver diseases in the world","volume":"70","author":"Asrani","year":"2019","journal-title":"J Hepatol"},{"issue":"1","key":"10.1016\/bs.pmbts.2026.02.004_bib3","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1002\/hep.28431","article-title":"Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes","volume":"64","author":"Younossi","year":"2016","journal-title":"Hepatology"},{"issue":"3","key":"10.1016\/bs.pmbts.2026.02.004_bib4","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1016\/j.jhep.2023.04.036","article-title":"Changing epidemiology, global trends and implications for outcomes of NAFLD","volume":"79","author":"Wong","year":"2023","journal-title":"J Hepatol"},{"issue":"6","key":"10.1016\/bs.pmbts.2026.02.004_bib5","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1038\/s41575-023-00759-2","article-title":"Global epidemiology of cirrhosis - aetiology, trends and predictions","volume":"20","author":"Huang","year":"2023","journal-title":"Nat Rev Gastroenterol Hepatol"},{"issue":"5","key":"10.1016\/bs.pmbts.2026.02.004_bib6","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1038\/ng.3252","article-title":"Exome sequencing of hepatocellular carcinomas identifies new mutational signatures and potential therapeutic targets","volume":"47","author":"Schulze","year":"2015","journal-title":"Nat Genet"},{"issue":"1","key":"10.1016\/bs.pmbts.2026.02.004_bib7","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1186\/s12876-025-03738-w","article-title":"Association between single nucleotide polymorphisms in PNPLA3, TM6SF2 and MBOAT7 genes and markers of cancer aggressiveness in a Sri Lankan NASH-related HCC cohort","volume":"25","author":"Samarasinghe","year":"2025","journal-title":"BMC Gastroenterol"},{"issue":"6","key":"10.1016\/bs.pmbts.2026.02.004_bib8","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/j.tig.2020.03.003","article-title":"The epigenetic drug discovery landscape for metabolic-associated fatty liver disease","volume":"36","author":"Bayoumi","year":"2020","journal-title":"Trends Genet"},{"issue":"1","key":"10.1016\/bs.pmbts.2026.02.004_bib9","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-018-31886-5","article-title":"Identification of differentially methylated region (DMR) networks associated with progression of nonalcoholic fatty liver disease","volume":"8","author":"Hotta","year":"2018","journal-title":"Sci Rep"},{"issue":"1","key":"10.1016\/bs.pmbts.2026.02.004_bib10","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1186\/s13072-023-00504-8","article-title":"Alterations in the hepatocyte epigenetic landscape in steatosis","volume":"16","author":"Maji","year":"2023","journal-title":"Epigenetics Chromatin"},{"issue":"2","key":"10.1016\/bs.pmbts.2026.02.004_bib11","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.cmet.2013.07.004","article-title":"DNA methylation analysis in nonalcoholic fatty liver disease suggests distinct disease-specific and remodeling signatures after bariatric surgery","volume":"18","author":"Ahrens","year":"2013","journal-title":"Cell Metab"},{"issue":"13","key":"10.1016\/bs.pmbts.2026.02.004_bib12","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1080\/17501911.2024.2374702","article-title":"Deciphering KDM8 dysregulation and CpG methylation in hepatocellular carcinoma using multi-omics and machine learning","volume":"16","author":"Ahmed","year":"2024","journal-title":"Epigenomics"},{"issue":"11","key":"10.1016\/bs.pmbts.2026.02.004_bib13","doi-asserted-by":"crossref","DOI":"10.1016\/j.celrep.2023.113446","article-title":"Genome-wide profiling of transcription factor activity in primary liver cancer using single-cell ATAC sequencing","volume":"42","author":"Craig","year":"2023","journal-title":"Cell Rep"},{"issue":"1","key":"10.1016\/bs.pmbts.2026.02.004_bib14","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1038\/s41597-023-02257-1","article-title":"GepLiver: an integrative liver expression atlas spanning developmental stages and liver disease phases","volume":"10","author":"Li","year":"2023","journal-title":"Sci Data"},{"key":"10.1016\/bs.pmbts.2026.02.004_bib15","article-title":"Developing deep learning-based strategies to predict the risk of hepatocellular carcinoma among patients with nonalcoholic fatty liver disease from electronic health records","author":"Li","year":"2023","journal-title":"medRxiv"},{"key":"10.1016\/bs.pmbts.2026.02.004_bib16","doi-asserted-by":"crossref","DOI":"10.1093\/database\/baaa012","article-title":"CancerLivER: a database of liver cancer gene expression resources and biomarkers","volume":"2020","author":"Kaur","year":"2020","journal-title":"Database (Oxford)"},{"issue":"5","key":"10.1016\/bs.pmbts.2026.02.004_bib17","doi-asserted-by":"crossref","first-page":"G1281","DOI":"10.1152\/ajpgi.00074.2008","article-title":"Gene expression in human NAFLD","volume":"294","author":"Greco","year":"2008","journal-title":"Am J Physiol Gastrointest Liver Physiol"},{"issue":"1","key":"10.1016\/bs.pmbts.2026.02.004_bib18","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s43856-025-00780-3","article-title":"Deep proteome profiling of metabolic dysfunction-associated steatotic liver disease","volume":"5","author":"Boel","year":"2025","journal-title":"Commun Med (Lond)"},{"issue":"3","key":"10.1016\/bs.pmbts.2026.02.004_bib19","doi-asserted-by":"crossref","DOI":"10.15252\/msb.20188793","article-title":"Plasma proteome profiling discovers novel proteins associated with non-alcoholic fatty liver disease","volume":"15","author":"Niu","year":"2019","journal-title":"Mol Syst Biol"},{"issue":"1","key":"10.1016\/bs.pmbts.2026.02.004_bib20","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-017-11759-z","article-title":"Identification of key metabolic changes during liver fibrosis progression in rats using a urine and serum metabolomics approach","volume":"7","author":"Chang","year":"2017","journal-title":"Sci Rep"},{"issue":"10","key":"10.1016\/bs.pmbts.2026.02.004_bib21","doi-asserted-by":"crossref","first-page":"1884","DOI":"10.1136\/gutjnl-2018-317584","article-title":"Serum metabolites detect the presence of advanced fibrosis in derivation and validation cohorts of patients with non-alcoholic fatty liver disease","volume":"68","author":"Caussy","year":"2019","journal-title":"Gut"},{"issue":"1","key":"10.1016\/bs.pmbts.2026.02.004_bib22","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1186\/s13040-024-00391-z","article-title":"Deep learning-based approaches for multi-omics data integration and analysis","volume":"17","author":"Ballard","year":"2024","journal-title":"BioData Min"},{"issue":"4","key":"10.1016\/bs.pmbts.2026.02.004_bib23","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1038\/s42255-023-00775-1","article-title":"A proteo-transcriptomic map of non-alcoholic fatty liver disease signatures","volume":"5","author":"Govaere","year":"2023","journal-title":"Nat Metab"},{"issue":"2","key":"10.1016\/bs.pmbts.2026.02.004_bib24","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pdig.0000447","article-title":"Identification of integrated proteomics and transcriptomics signature of alcohol-associated liver disease using machine learning","volume":"3","author":"Listopad","year":"2024","journal-title":"PLOS Digit Health"},{"issue":"7","key":"10.1016\/bs.pmbts.2026.02.004_bib25","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1011224","article-title":"Ten quick tips for avoiding pitfalls in multi-omics data integration analyses","volume":"19","author":"Chicco","year":"2023","journal-title":"PLoS Comput Biol"},{"issue":"6","key":"10.1016\/bs.pmbts.2026.02.004_bib26","doi-asserted-by":"crossref","DOI":"10.15252\/msb.20178124","article-title":"Multi-omics factor analysis-a framework for unsupervised integration of multi-omics data sets","volume":"14","author":"Argelaguet","year":"2018","journal-title":"Mol Syst Biol"},{"issue":"3","key":"10.1016\/bs.pmbts.2026.02.004_bib27","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1038\/s41587-022-01520-x","article-title":"Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models","volume":"41","author":"Allesoe","year":"2023","journal-title":"Nat Biotechnol"},{"issue":"1","key":"10.1016\/bs.pmbts.2026.02.004_bib28","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1186\/s12859-025-06245-7","article-title":"Autoencoders with shared and specific embeddings for multi-omics data integration","volume":"26","author":"Wang","year":"2025","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"10.1016\/bs.pmbts.2026.02.004_bib29","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1186\/s12859-025-06248-4","article-title":"fuseMLR: an R package for integrative prediction modeling of multi-omics data","volume":"26","author":"Fouodo","year":"2025","journal-title":"BMC Bioinformatics"},{"key":"10.1016\/bs.pmbts.2026.02.004_bib30","doi-asserted-by":"crossref","DOI":"10.3389\/fgene.2024.1425456","article-title":"Methods for multi-omic data integration in cancer research","volume":"15","author":"Hernandez-Lemus","year":"2024","journal-title":"Front Genet"},{"issue":"4","key":"10.1016\/bs.pmbts.2026.02.004_bib31","doi-asserted-by":"crossref","DOI":"10.3390\/jpm12040601","article-title":"Machine-learning-based late fusion on multi-omics and multi-scale data for non-small-cell lung cancer diagnosis","volume":"12","author":"Carrillo-Perez","year":"2022","journal-title":"J Pers Med"},{"issue":"6","key":"10.1016\/bs.pmbts.2026.02.004_bib32","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1038\/nrg3920","article-title":"Machine learning applications in genetics and genomics","volume":"16","author":"Libbrecht","year":"2015","journal-title":"Nat Rev Genet"},{"issue":"3","key":"10.1016\/bs.pmbts.2026.02.004_bib33","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1093\/evlett\/qrad064","article-title":"Regularized regression can improve estimates of multivariate selection in the face of multicollinearity and limited data","volume":"8","author":"Sztepanacz","year":"2024","journal-title":"Evol Lett"},{"issue":"1","key":"10.1016\/bs.pmbts.2026.02.004_bib34","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1186\/s12967-024-04940-2","article-title":"Ensemble methods of rank-based trees for single sample classification with gene expression profiles","volume":"22","author":"Lu","year":"2024","journal-title":"J Transl Med"},{"key":"10.1016\/bs.pmbts.2026.02.004_bib35","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s13040-017-0126-8","article-title":"Meta-analytic support vector machine for integrating multiple omics data","volume":"10","author":"Kim","year":"2017","journal-title":"BioData Min"},{"issue":"24","key":"10.1016\/bs.pmbts.2026.02.004_bib36","first-page":"2547","article-title":"Explainable hierarchical clustering for patient subtyping and risk prediction","volume":"248","author":"Werner","year":"2023","journal-title":"Exp Biol Med (Maywood)"},{"key":"10.1016\/bs.pmbts.2026.02.004_bib37","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.3025","article-title":"Comprehensive review of dimensionality reduction algorithms: challenges, limitations, and innovative solutions","volume":"11","author":"Wani","year":"2025","journal-title":"PeerJ Comput Sci"},{"issue":"1","key":"10.1016\/bs.pmbts.2026.02.004_bib38","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat Mach Intell"},{"issue":"4","key":"10.1016\/bs.pmbts.2026.02.004_bib39","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1038\/s41416-022-02048-2","article-title":"Integrative analysis of multi-omics data for liquid biopsy","volume":"128","author":"Chen","year":"2023","journal-title":"Br J Cancer"},{"issue":"2","key":"10.1016\/bs.pmbts.2026.02.004_bib40","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaf168","article-title":"Assessing the impact of batch effect associated missing values on downstream analysis in high-throughput biomedical data","volume":"26","author":"Hui","year":"2025","journal-title":"Brief Bioinform"},{"issue":"1","key":"10.1016\/bs.pmbts.2026.02.004_bib41","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1186\/s13059-024-03401-9","article-title":"Assessing and mitigating batch effects in large-scale omics studies","volume":"25","author":"Yu","year":"2024","journal-title":"Genome Biol"},{"issue":"1","key":"10.1016\/bs.pmbts.2026.02.004_bib42","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1038\/s41467-024-55325-4","article-title":"Spatial transcriptomics of healthy and fibrotic human liver at single-cell resolution","volume":"16","author":"Watson","year":"2025","journal-title":"Nat Commun"},{"issue":"2","key":"10.1016\/bs.pmbts.2026.02.004_bib43","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.iliver.2022.07.001","article-title":"Radiomics\/Radiogenomics in hepatocellular carcinoma: applications and challenges in interventional management","volume":"1","author":"Fu","year":"2022","journal-title":"ILIVER"},{"issue":"9","key":"10.1016\/bs.pmbts.2026.02.004_bib44","doi-asserted-by":"crossref","first-page":"2050","DOI":"10.1111\/liv.14555","article-title":"Radiomics in liver diseases: current progress and future opportunities","volume":"40","author":"Wei","year":"2020","journal-title":"Liver Int"},{"issue":"4","key":"10.1016\/bs.pmbts.2026.02.004_bib45","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btad135","article-title":"Graph convolutional network-based feature selection for high-dimensional and low-sample size data","volume":"39","author":"Chen","year":"2023","journal-title":"Bioinformatics"},{"issue":"6","key":"10.1016\/bs.pmbts.2026.02.004_bib46","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac409","article-title":"BioGPT: generative pre-trained transformer for biomedical text generation and mining","volume":"23","author":"Luo","year":"2022","journal-title":"Brief Bioinform"},{"issue":"3","key":"10.1016\/bs.pmbts.2026.02.004_bib47","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","article-title":"Federated learning: challenges, methods, and future directions","volume":"37","author":"Li","year":"2020","journal-title":"IEEE Signal Processing Magazine"},{"issue":"10","key":"10.1016\/bs.pmbts.2026.02.004_bib48","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1038\/ng.2764","article-title":"The cancer genome atlas pan-cancer analysis project","volume":"45","author":"Cancer Genome Atlas Research","year":"2013","journal-title":"Nat Genet"},{"key":"10.1016\/bs.pmbts.2026.02.004_bib49","doi-asserted-by":"crossref","first-page":"D991","DOI":"10.1093\/nar\/gks1193","article-title":"NCBI GEO: archive for functional genomics data sets--update","volume":"41","author":"Barrett","year":"2013","journal-title":"Nucleic Acids Res"},{"key":"10.1016\/bs.pmbts.2026.02.004_bib50","article-title":"A human pan-disease blood atlas of the circulating proteome","author":"Alvez","year":"2025","journal-title":"Science"},{"issue":"6","key":"10.1016\/bs.pmbts.2026.02.004_bib51","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1038\/ng.2653","article-title":"The genotype-tissue expression (GTEx) project","volume":"45","author":"Consortium","year":"2013","journal-title":"Nat Genet"},{"key":"10.1016\/bs.pmbts.2026.02.004_bib52","doi-asserted-by":"crossref","first-page":"2740","DOI":"10.1016\/j.csbj.2024.06.031","article-title":"LiverSCA: a comprehensive and user-friendly cell atlas in human hepatocellular carcinoma","volume":"23","author":"Long","year":"2024","journal-title":"Comput Struct Biotechnol J"},{"issue":"4","key":"10.1016\/bs.pmbts.2026.02.004_bib53","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1111\/liv.12292","article-title":"CellMinerHCC: a microarray-based expression database for hepatocellular carcinoma cell lines","volume":"34","author":"Staib","year":"2014","journal-title":"Liver Int"},{"issue":"5","key":"10.1016\/bs.pmbts.2026.02.004_bib54","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1158\/2159-8290.CD-12-0095","article-title":"The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data","volume":"2","author":"Cerami","year":"2012","journal-title":"Cancer Discov"},{"issue":"7484","key":"10.1016\/bs.pmbts.2026.02.004_bib55","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1038\/nature12912","article-title":"Discovery and saturation analysis of cancer genes across 21 tumour types","volume":"505","author":"Lawrence","year":"2014","journal-title":"Nature"},{"key":"10.1016\/bs.pmbts.2026.02.004_bib56","article-title":"SomaMutDB 2.0: a comprehensive database for exploring somatic mutations and their functional impact in normal human tissues","author":"Shea","year":"2025","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"10.1016\/bs.pmbts.2026.02.004_bib57","doi-asserted-by":"crossref","first-page":"D1210","DOI":"10.1093\/nar\/gkad986","article-title":"COSMIC: a curated database of somatic variants and clinical data for cancer","volume":"52","author":"Sondka","year":"2024","journal-title":"Nucleic Acids Res"},{"issue":"2","key":"10.1016\/bs.pmbts.2026.02.004_bib58","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1038\/ng.3774","article-title":"CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer","volume":"49","author":"Griffith","year":"2017","journal-title":"Nat Genet"}],"container-title":["Progress in Molecular Biology and Translational Science","Artificial Intelligence for Multi-omics Analysis Part B"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1877117326001031?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1877117326001031?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T11:32:44Z","timestamp":1779363164000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1877117326001031"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9780443413490"],"references-count":58,"URL":"https:\/\/doi.org\/10.1016\/bs.pmbts.2026.02.004","relation":{},"ISSN":["1877-1173"],"issn-type":[{"value":"1877-1173","type":"print"}],"subject":[],"published":{"date-parts":[[2026]]}}}