{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T18:31:55Z","timestamp":1783967515478,"version":"3.55.0"},"reference-count":100,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"name":"Shaanxi Provincial Outstanding Youth Fund","award":["2024JC-JCQN-77"],"award-info":[{"award-number":["2024JC-JCQN-77"]}]},{"DOI":"10.13039\/501100007547","name":"Fourth Military Medical University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100007547","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82372693"],"award-info":[{"award-number":["82372693"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82073210"],"award-info":[{"award-number":["82073210"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82303427"],"award-info":[{"award-number":["82303427"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007547","name":"Fourth Military Medical University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100007547","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pathology - Research and Practice"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1016\/j.prp.2025.156083","type":"journal-article","created":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T02:42:54Z","timestamp":1749782574000},"page":"156083","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":4,"special_numbering":"C","title":["Integration of machine learning in biomarker discovery for esophageal squamous cell carcinoma: Applications and future directions"],"prefix":"10.1016","volume":"272","author":[{"given":"Wanli","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lili","family":"Duan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinhui","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liaoran","family":"Niu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenyang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daiming","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8276-2345","authenticated-orcid":false,"given":"Liu","family":"Hong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.prp.2025.156083_bib1","first-page":"12","article-title":"Cancer statistics, 2024","volume":"74","author":"Siegel","year":"2024","journal-title":"CA Cancer J. Clin."},{"key":"10.1016\/j.prp.2025.156083_bib2","doi-asserted-by":"crossref","first-page":"393","DOI":"10.6004\/jnccn.2023.0019","article-title":"Esophageal and esophagogastric junction cancers, version 2.2023, NCCN clinical practice guidelines in oncology","volume":"21","author":"Ajani","year":"2023","journal-title":"J. Natl. Compr. Cancer Netw."},{"key":"10.1016\/j.prp.2025.156083_bib3","doi-asserted-by":"crossref","DOI":"10.3390\/cancers15235604","article-title":"Overview of risk factors for esophageal squamous cell carcinoma in China","volume":"15","author":"Conway","year":"2023","journal-title":"Cancers"},{"key":"10.1016\/j.prp.2025.156083_bib4","doi-asserted-by":"crossref","first-page":"1447","DOI":"10.1002\/ijc.34158","article-title":"Global and national trends in the age-specific sex ratio of esophageal cancer and gastric cancer by subtype","volume":"151","author":"Wang","year":"2022","journal-title":"Int J. Cancer"},{"key":"10.1016\/j.prp.2025.156083_bib5","article-title":"Effectiveness of endoscopic screening on esophageal cancer incidence and mortality: a 9-Year report of the endoscopic screening for esophageal cancer in China (ESECC) randomized trial","author":"Liu","year":"2024","journal-title":"J. Clin. Oncol."},{"key":"10.1016\/j.prp.2025.156083_bib6","doi-asserted-by":"crossref","first-page":"04025","DOI":"10.7189\/jogh.14.04025","article-title":"Burdens of stomach and esophageal cancer from 1990 to 2019 and projection to 2030 in China: findings from the 2019 global burden of disease study","volume":"14","author":"Jiang","year":"2024","journal-title":"J. Glob. Health"},{"key":"10.1016\/j.prp.2025.156083_bib7","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1007\/s12672-025-01816-9","article-title":"Next-generation sequencing in cancer diagnosis and treatment: clinical applications and future directions","volume":"16","author":"Ghoreyshi","year":"2025","journal-title":"Discov. Oncol."},{"key":"10.1016\/j.prp.2025.156083_bib8","doi-asserted-by":"crossref","DOI":"10.3390\/cells10113169","article-title":"Research progress of gliomas in machine learning","volume":"10","author":"Wu","year":"2021","journal-title":"Cells"},{"key":"10.1016\/j.prp.2025.156083_bib9","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.ccell.2022.01.012","article-title":"New genomic technologies for multi-cancer early detection: rethinking the scope of cancer screening","volume":"40","author":"Hackshaw","year":"2022","journal-title":"Cancer Cell"},{"issue":"2024","key":"10.1016\/j.prp.2025.156083_bib10","article-title":"Targeting esophageal carcinoma: molecular mechanisms and clinical studies","volume":"5","author":"Wang","year":"2020","journal-title":"MedComm"},{"key":"10.1016\/j.prp.2025.156083_bib11","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.semcancer.2024.02.001","article-title":"Extracellular vesicles associated microRNAs: their biology and clinical significance as biomarkers in gastrointestinal cancers","volume":"99","author":"Li","year":"2024","journal-title":"Semin Cancer Biol."},{"key":"10.1016\/j.prp.2025.156083_bib12","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1186\/s40364-023-00535-z","article-title":"Biomarkers and prognostic factors of PD-1\/PD-L1 inhibitor-based therapy in patients with advanced hepatocellular carcinoma","volume":"12","author":"Zhang","year":"2024","journal-title":"Biomark. Res"},{"key":"10.1016\/j.prp.2025.156083_bib13","article-title":"Advances in machine learning processing of big data from disease diagnosis sensors","author":"Lu","year":"2024","journal-title":"ACS Sens"},{"key":"10.1016\/j.prp.2025.156083_bib14","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1016\/j.gpb.2022.11.003","article-title":"Machine learning for lung cancer diagnosis, treatment, and prognosis","volume":"20","author":"Li","year":"2022","journal-title":"Genom. Proteom. Bioinforma."},{"key":"10.1016\/j.prp.2025.156083_bib15","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1186\/s13000-024-01453-w","article-title":"Artificial intelligence's impact on breast cancer pathology: a literature review","volume":"19","author":"Soliman","year":"2024","journal-title":"Diagn. Pathol."},{"key":"10.1016\/j.prp.2025.156083_bib16","first-page":"41","article-title":"Applications of support vector machine (SVM) learning in cancer genomics","volume":"15","author":"Huang","year":"2018","journal-title":"Cancer Genom. Proteom."},{"key":"10.1016\/j.prp.2025.156083_bib17","doi-asserted-by":"crossref","DOI":"10.7759\/cureus.53024","article-title":"Use of artificial intelligence in the diagnosis of colorectal cancer","author":"Nduma","year":"2024","journal-title":"Cureus 16"},{"key":"10.1016\/j.prp.2025.156083_bib18","doi-asserted-by":"crossref","first-page":"631","DOI":"10.2174\/18735592MTEzsMDMnz","article-title":"Artificial intelligence, big data and machine learning approaches in precision Medicine & drug discovery","volume":"22","author":"Nayarisseri","year":"2021","journal-title":"Curr. Drug Targets"},{"key":"10.1016\/j.prp.2025.156083_bib19","article-title":"Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: challenges and opportunities","volume":"15","author":"Gonzalez","year":"2024","journal-title":"J. Pathol. Inf."},{"key":"10.1016\/j.prp.2025.156083_bib20","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2021.104248","article-title":"Convolutional neural networks for breast cancer detection in mammography: a survey","volume":"131","author":"Abdelrahman","year":"2021","journal-title":"Comput. Biol. Med"},{"key":"10.1016\/j.prp.2025.156083_bib21","doi-asserted-by":"crossref","DOI":"10.3390\/diagnostics14040454","article-title":"Skin cancer detection and classification using neural network algorithms: a systematic review","volume":"14","author":"Hermosilla","year":"2024","journal-title":"Diagnostics"},{"key":"10.1016\/j.prp.2025.156083_bib22","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.path.2020.01.002","article-title":"Unsupervised machine learning in pathology: the next frontier","volume":"13","author":"Roohi","year":"2020","journal-title":"Surg. Pathol. Clin."},{"key":"10.1016\/j.prp.2025.156083_bib23","article-title":"Applying multivariate adaptive splines to identify genes with expressions varying after diagnosis in microarray experiments","volume":"16","author":"Duan","year":"2017","journal-title":"Cancer Inf."},{"key":"10.1016\/j.prp.2025.156083_bib24","first-page":"E220","article-title":"Factors underlying treatment decision-making for localized prostate cancer in the U.S. And Canada: a scoping review using principal component analysis","volume":"13","author":"Andkhoie","year":"2019","journal-title":"Can. Urol. Assoc. J."},{"key":"10.1016\/j.prp.2025.156083_bib25","doi-asserted-by":"crossref","first-page":"4538","DOI":"10.1016\/j.csbj.2021.08.011","article-title":"A review on machine learning approaches and trends in drug discovery","volume":"19","author":"Carracedo-Reboredo","year":"2021","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"10.1016\/j.prp.2025.156083_bib26","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107338","article-title":"Predicting lung cancer survival based on clinical data using machine learning: a review","volume":"165","author":"Altuhaifa","year":"2023","journal-title":"Comput. Biol. Med"},{"key":"10.1016\/j.prp.2025.156083_bib27","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1007\/s00464-022-09516-z","article-title":"Machine learning applications in upper gastrointestinal cancer surgery: a systematic review","volume":"37","author":"Bekta\u015f","year":"2023","journal-title":"Surg. Endosc."},{"key":"10.1016\/j.prp.2025.156083_bib28","doi-asserted-by":"crossref","first-page":"1772","DOI":"10.1016\/j.cell.2023.01.035","article-title":"From patterns to patients: advances in clinical machine learning for cancer diagnosis, prognosis, and treatment","volume":"186","author":"Swanson","year":"2023","journal-title":"Cell"},{"key":"10.1016\/j.prp.2025.156083_bib29","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1186\/s13073-021-00968-x","article-title":"Deep learning in cancer diagnosis, prognosis and treatment selection","volume":"13","author":"Tran","year":"2021","journal-title":"Genome Med."},{"key":"10.1016\/j.prp.2025.156083_bib30","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105458","article-title":"Machine learning in medical applications: a review of state-of-the-art methods","volume":"145","author":"Shehab","year":"2022","journal-title":"Comput. Biol. Med"},{"key":"10.1016\/j.prp.2025.156083_bib31","doi-asserted-by":"crossref","first-page":"6390","DOI":"10.1109\/TNNLS.2021.3136503","article-title":"DeepSMOTE: fusing deep learning and SMOTE for imbalanced data","volume":"34","author":"Dablain","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn Syst."},{"key":"10.1016\/j.prp.2025.156083_bib32","first-page":"165","article-title":"Improving the prediction of survival in cancer patients by using machine learning techniques: experience of gene expression data: a narrative review","volume":"46","author":"Bashiri","year":"2017","journal-title":"Iran. J. Public Health"},{"key":"10.1016\/j.prp.2025.156083_bib33","doi-asserted-by":"crossref","first-page":"5008","DOI":"10.1016\/j.csbj.2021.09.001","article-title":"Machine learning and deep learning methods that use omics data for metastasis prediction","volume":"19","author":"Albaradei","year":"2021","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"10.1016\/j.prp.2025.156083_bib34","doi-asserted-by":"crossref","first-page":"10473","DOI":"10.1007\/s00432-023-04956-z","article-title":"Machine learning and deep learning techniques for breast cancer diagnosis and classification: a comprehensive review of medical imaging studies","volume":"149","author":"Radak","year":"2023","journal-title":"J. Cancer Res Clin. Oncol."},{"key":"10.1016\/j.prp.2025.156083_bib35","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1111\/jgh.15502","article-title":"Machine learning on microbiome research in gastrointestinal cancer","volume":"36","author":"Cheung","year":"2021","journal-title":"J. Gastroenterol. Hepatol."},{"key":"10.1016\/j.prp.2025.156083_bib36","doi-asserted-by":"crossref","DOI":"10.1016\/j.bbcan.2021.188588","article-title":"Machine learning in epigenomics: insights into cancer biology and Medicine","volume":"1876","author":"Arslan","year":"2021","journal-title":"Biochim Biophys. Acta Rev. Cancer"},{"key":"10.1016\/j.prp.2025.156083_bib37","doi-asserted-by":"crossref","DOI":"10.3390\/bioengineering10020173","article-title":"Machine learning methods for cancer classification using gene expression data: a review","volume":"10","author":"Alharbi","year":"2023","journal-title":"Bioengineering"},{"key":"10.1016\/j.prp.2025.156083_bib38","first-page":"1","article-title":"The intrapapillary capillary loop (IPCL) changes in superficial esophageal lesions","volume":"30","author":"Wu","year":"2017","journal-title":"Dis. Esophagus"},{"key":"10.1016\/j.prp.2025.156083_bib39","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1038\/s41420-024-01864-0","article-title":"IGF-1-mediated FOXC1 overexpression induces stem-like properties through upregulating CBX7 and IGF-1R in esophageal squamous cell carcinoma","volume":"10","author":"Wu","year":"2024","journal-title":"Cell Death Discov."},{"key":"10.1016\/j.prp.2025.156083_bib40","doi-asserted-by":"crossref","DOI":"10.3390\/ijms24043316","article-title":"Biomarkers for early detection, prognosis, and therapeutics of esophageal cancers","volume":"24","author":"Rai","year":"2023","journal-title":"Int. J. Mol. Sci."},{"key":"10.1016\/j.prp.2025.156083_bib41","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1080\/14737159.2019.1563485","article-title":"Advances in prognostic biomarkers for esophageal cancer","volume":"19","author":"Yang","year":"2019","journal-title":"Expert Rev. Mol. Diagn."},{"key":"10.1016\/j.prp.2025.156083_bib42","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1080\/17474124.2017.1372189","article-title":"Molecular mechanisms and clinical implications of miRNAs in drug resistance of esophageal cancer","volume":"11","author":"Yang","year":"2017","journal-title":"Expert Rev. Gastroenterol. Hepatol."},{"key":"10.1016\/j.prp.2025.156083_bib43","doi-asserted-by":"crossref","first-page":"1909","DOI":"10.1136\/gutjnl-2021-326271","article-title":"Artificial intelligence in gastroenterology and hepatology: how to advance clinical practice while ensuring health equity","volume":"71","author":"Uche-Anya","year":"2022","journal-title":"Gut"},{"key":"10.1016\/j.prp.2025.156083_bib44","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.semcancer.2023.02.009","article-title":"Towards artificial intelligence to multi-omics characterization of tumor heterogeneity in esophageal cancer","volume":"91","author":"Li","year":"2023","journal-title":"Semin Cancer Biol."},{"key":"10.1016\/j.prp.2025.156083_bib45","doi-asserted-by":"crossref","first-page":"18098","DOI":"10.1002\/jcp.28442","article-title":"Identification of esophageal cancer pathway deviation and construction of a diagnosis model using three kernel genes","volume":"234","author":"Xue","year":"2019","journal-title":"J. Cell Physiol."},{"key":"10.1016\/j.prp.2025.156083_bib46","first-page":"6732780","article-title":"Machine learning and novel biomarkers associated with immune infiltration for the diagnosis of esophageal squamous cell carcinoma","volume":"2022","author":"Zhang","year":"2022","journal-title":"J. Oncol."},{"key":"10.1016\/j.prp.2025.156083_bib47","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2024.108077","article-title":"A pathway-based computational framework for identification of a new modal of multi-omics biomarkers and its application in esophageal cancer","volume":"247","author":"Zhou","year":"2024","journal-title":"Comput. Methods Prog. Biomed."},{"key":"10.1016\/j.prp.2025.156083_bib48","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1186\/s13148-017-0430-7","article-title":"Targeted bisulfite sequencing identified a panel of DNA methylation-based biomarkers for esophageal squamous cell carcinoma (ESCC)","volume":"9","author":"Pu","year":"2017","journal-title":"Clin. Epigenet."},{"key":"10.1016\/j.prp.2025.156083_bib49","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1038\/s41416-021-01395-w","article-title":"Identification of diagnostic markers and lipid dysregulation in oesophageal squamous cell carcinoma through lipidomic analysis and machine learning","volume":"125","author":"Yuan","year":"2021","journal-title":"Br. J. Cancer"},{"key":"10.1016\/j.prp.2025.156083_bib50","doi-asserted-by":"crossref","first-page":"7590","DOI":"10.1038\/s41598-021-87037-w","article-title":"Mining the plasma-proteome associated genes in patients with gastro-esophageal cancers for biomarker discovery","volume":"11","author":"Vizeacoumar","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.prp.2025.156083_bib51","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0161407","article-title":"Review of predictions of survival in palliative care: how accurate are clinicians and who are the experts?","volume":"11","author":"White","year":"2016","journal-title":"PLoS One"},{"key":"10.1016\/j.prp.2025.156083_bib52","first-page":"22","article-title":"The accuracy of clinicians' predictions of survival in advanced cancer: a review","volume":"5","author":"Cheon","year":"2016","journal-title":"Ann. Palliat. Med."},{"key":"10.1016\/j.prp.2025.156083_bib53","doi-asserted-by":"crossref","first-page":"1974","DOI":"10.1515\/cclm-2022-0291","article-title":"Artificial intelligence in clinical applications for lung cancer: diagnosis, treatment and prognosis","volume":"60","author":"Pei","year":"2022","journal-title":"Clin. Chem. Lab. Med."},{"key":"10.1016\/j.prp.2025.156083_bib54","doi-asserted-by":"crossref","first-page":"1079795","DOI":"10.3389\/fgene.2023.1079795","article-title":"Identification of m6a-related signature genes in esophageal squamous cell carcinoma by machine learning method","volume":"14","author":"Shang","year":"2023","journal-title":"Front. Genet."},{"key":"10.1016\/j.prp.2025.156083_bib55","doi-asserted-by":"crossref","DOI":"10.1177\/15330338221117003","article-title":"Weighted correlation network analysis of cancer stem Cell-Related prognostic biomarkers in esophageal squamous cell carcinoma","volume":"21","author":"Zhao","year":"2022","journal-title":"Technol. Cancer Res. Treat."},{"key":"10.1016\/j.prp.2025.156083_bib56","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1186\/s12885-021-08647-1","article-title":"Using a machine learning approach to identify key prognostic molecules for esophageal squamous cell carcinoma","volume":"21","author":"Li","year":"2021","journal-title":"BMC Cancer"},{"key":"10.1016\/j.prp.2025.156083_bib57","doi-asserted-by":"crossref","first-page":"19847","DOI":"10.1038\/s41598-019-56367-1","article-title":"Characterization of a five-microRNA signature as a prognostic biomarker for esophageal squamous cell carcinoma","volume":"9","author":"Yu","year":"2019","journal-title":"Sci. Rep."},{"key":"10.1016\/j.prp.2025.156083_bib58","doi-asserted-by":"crossref","first-page":"387","DOI":"10.3892\/ol.2020.12250","article-title":"A model for predicting prognosis in patients with esophageal squamous cell carcinoma based on joint representation learning","volume":"20","author":"Yu","year":"2020","journal-title":"Oncol. Lett."},{"key":"10.1016\/j.prp.2025.156083_bib59","doi-asserted-by":"crossref","first-page":"3869825","DOI":"10.1155\/2019\/3869825","article-title":"Signature predicts prognosis in esophageal squamous cell carcinoma","volume":"2019","author":"Peng","year":"2019","journal-title":"Biomed. Res. Int."},{"issue":"13 (","key":"10.1016\/j.prp.2025.156083_bib60","article-title":"Using genomics feature selection method in radiomics pipeline improves prognostication performance in locally advanced esophageal squamous cell carcinoma-a pilot study","author":"Xie","year":"2021","journal-title":"Cancers"},{"key":"10.1016\/j.prp.2025.156083_bib61","doi-asserted-by":"crossref","DOI":"10.3390\/cancers14204970","article-title":"Genome-Wide DNA methylation and gene expression profiling characterizes molecular subtypes of esophagus squamous cell carcinoma for predicting patient survival and immunotherapy efficacy","volume":"14","author":"Zheng","year":"2022","journal-title":"Cancers"},{"key":"10.1016\/j.prp.2025.156083_bib62","doi-asserted-by":"crossref","DOI":"10.1016\/j.phrs.2020.105144","article-title":"Integrative stemness characteristics associated with prognosis and the immune microenvironment in esophageal cancer","volume":"161","author":"Yi","year":"2020","journal-title":"Pharm. Res."},{"key":"10.1016\/j.prp.2025.156083_bib63","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1111\/jcmm.14779","article-title":"Four transcription profile-based models identify novel prognostic signatures in oesophageal cancer","volume":"24","author":"Liu","year":"2020","journal-title":"J. Cell Mol. Med."},{"key":"10.1016\/j.prp.2025.156083_bib64","doi-asserted-by":"crossref","first-page":"1195195","DOI":"10.3389\/fphar.2023.1195195","article-title":"A tricarboxylic acid cycle-based machine learning model to select effective drug targets for the treatment of esophageal squamous cell carcinoma","volume":"14","author":"Liang","year":"2023","journal-title":"Front. Pharm."},{"key":"10.1016\/j.prp.2025.156083_bib65","doi-asserted-by":"crossref","DOI":"10.3389\/fgene.2021.671639","article-title":"Identification of key genes and pathways associated with paclitaxel resistance in esophageal squamous cell carcinoma based on bioinformatics analysis","volume":"12","author":"Shen","year":"2021","journal-title":"Front. Genet."},{"key":"10.1016\/j.prp.2025.156083_bib66","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1038\/s41392-020-00323-3","article-title":"Advances in targeted therapy for esophageal cancer","volume":"5","author":"Yang","year":"2020","journal-title":"Signal Transduct. Target Ther."},{"key":"10.1016\/j.prp.2025.156083_bib67","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.semcancer.2019.12.011","article-title":"Machine and deep learning approaches for cancer drug repurposing","volume":"68","author":"Issa","year":"2021","journal-title":"Semin. Cancer Biol."},{"key":"10.1016\/j.prp.2025.156083_bib68","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1038\/s41392-022-00994-0","article-title":"Artificial intelligence in cancer target identification and drug discovery","volume":"7","author":"You","year":"2022","journal-title":"Signal Transduct. Target Ther."},{"key":"10.1016\/j.prp.2025.156083_bib69","doi-asserted-by":"crossref","DOI":"10.3389\/fcell.2021.667852","article-title":"Metabolic and immunological subtypes of esophageal cancer reveal potential therapeutic opportunities","volume":"9","author":"King","year":"2021","journal-title":"Front. Cell Dev. Biol."},{"key":"10.1016\/j.prp.2025.156083_bib70","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1038\/s41573-019-0024-5","article-title":"Applications of machine learning in drug discovery and development","volume":"18","author":"Vamathevan","year":"2019","journal-title":"Nat. Rev. Drug Discov."},{"key":"10.1016\/j.prp.2025.156083_bib71","doi-asserted-by":"crossref","DOI":"10.3390\/molecules25225277","article-title":"Machine learning methods in drug discovery","volume":"25","author":"Patel","year":"2020","journal-title":"Molecules"},{"key":"10.1016\/j.prp.2025.156083_bib72","doi-asserted-by":"crossref","DOI":"10.3390\/ijms24032026","article-title":"Artificial intelligence and machine learning technology driven modern drug discovery and development","volume":"24","author":"Sarkar","year":"2023","journal-title":"Int. J. Mol. Sci."},{"key":"10.1016\/j.prp.2025.156083_bib73","doi-asserted-by":"crossref","first-page":"2209","DOI":"10.1016\/j.drudis.2022.03.017","article-title":"Machine learning guided early drug discovery of small molecules","volume":"27","author":"Pillai","year":"2022","journal-title":"Drug Discov. Today"},{"key":"10.1016\/j.prp.2025.156083_bib74","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1016\/j.drudis.2020.12.003","article-title":"Advanced machine-learning techniques in drug discovery","volume":"26","author":"Elbadawi","year":"2021","journal-title":"Drug Discov. Today"},{"key":"10.1016\/j.prp.2025.156083_bib75","doi-asserted-by":"crossref","first-page":"1285795","DOI":"10.3389\/fmolb.2023.1285795","article-title":"Machine learning for pan-cancer classification based on RNA sequencing data","volume":"10","author":"\u0160tancl","year":"2023","journal-title":"Front. Mol. Biosci."},{"key":"10.1016\/j.prp.2025.156083_bib76","doi-asserted-by":"crossref","first-page":"5546","DOI":"10.1016\/j.csbj.2021.10.006","article-title":"Applied machine learning in cancer research: a systematic review for patient diagnosis, classification and prognosis","volume":"19","author":"Kourou","year":"2021","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"10.1016\/j.prp.2025.156083_bib77","doi-asserted-by":"crossref","first-page":"2057","DOI":"10.1038\/s41591-023-02482-6","article-title":"Machine learning for genetics-based classification and treatment response prediction in cancer of unknown primary","volume":"29","author":"Moon","year":"2023","journal-title":"Nat. Med."},{"key":"10.1016\/j.prp.2025.156083_bib78","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1186\/s12859-022-04720-z","article-title":"Machine learning approach informs biology of cancer drug response","volume":"23","author":"Zhu","year":"2022","journal-title":"BMC Bioinforma."},{"key":"10.1016\/j.prp.2025.156083_bib79","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1093\/bib\/bbz171","article-title":"Deep learning for drug response prediction in cancer","volume":"22","author":"Baptista","year":"2021","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.prp.2025.156083_bib80","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0186906","article-title":"Open source machine-learning algorithms for the prediction of optimal cancer drug therapies","volume":"12","author":"Huang","year":"2017","journal-title":"PLoS One"},{"key":"10.1016\/j.prp.2025.156083_bib81","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btad491","article-title":"Bayesian multitask learning for Medicine recommendation based on online patient reviews","volume":"39","author":"Cheng","year":"2023","journal-title":"Bioinformatics"},{"key":"10.1016\/j.prp.2025.156083_bib82","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1038\/s41467-024-45589-1","article-title":"Regression-based deep-learning predicts molecular biomarkers from pathology slides","volume":"15","author":"El Nahhas","year":"2024","journal-title":"Nat. Commun."},{"key":"10.1016\/j.prp.2025.156083_bib83","doi-asserted-by":"crossref","first-page":"3602","DOI":"10.1245\/s10434-007-9550-1","article-title":"Pretreatment gene expression profiles can be used to predict response to neoadjuvant chemoradiotherapy in esophageal cancer","volume":"14","author":"Duong","year":"2007","journal-title":"Ann. Surg. Oncol."},{"key":"10.1016\/j.prp.2025.156083_bib84","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.csbj.2014.11.005","article-title":"Machine learning applications in cancer prognosis and prediction","volume":"13","author":"Kourou","year":"2015","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"10.1016\/j.prp.2025.156083_bib85","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1080\/17460441.2021.1918096","article-title":"Artificial intelligence in early drug discovery enabling precision medicine","volume":"16","author":"Boniolo","year":"2021","journal-title":"Expert Opin. Drug Discov."},{"key":"10.1016\/j.prp.2025.156083_bib86","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1002\/gcc.23177","article-title":"Machine learning in computational histopathology: challenges and opportunities","volume":"62","author":"Cooper","year":"2023","journal-title":"Genes Chromosomes Cancer"},{"key":"10.1016\/j.prp.2025.156083_bib87","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1208\/s12248-020-00494-5","article-title":"Challenge-Enabled machine learning to drug-response prediction","volume":"22","author":"Wang","year":"2020","journal-title":"Aaps J."},{"key":"10.1016\/j.prp.2025.156083_bib88","doi-asserted-by":"crossref","DOI":"10.3390\/molecules29040903","article-title":"Machine learning empowering drug discovery: applications, opportunities and challenges","volume":"29","author":"Qi","year":"2024","journal-title":"Molecules"},{"key":"10.1016\/j.prp.2025.156083_bib89","doi-asserted-by":"crossref","first-page":"8736","DOI":"10.1021\/acs.chemrev.3c00189","article-title":"Machine learning methods for small data challenges in molecular science","volume":"123","author":"Dou","year":"2023","journal-title":"Chem. Rev."},{"key":"10.1016\/j.prp.2025.156083_bib90","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1016\/j.csbj.2021.06.030","article-title":"Integration strategies of multi-omics data for machine learning analysis","volume":"19","author":"Picard","year":"2021","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"10.1016\/j.prp.2025.156083_bib91","doi-asserted-by":"crossref","DOI":"10.2196\/22394","article-title":"Radiomic and genomic machine learning method performance for prostate cancer diagnosis: systematic literature review","volume":"23","author":"Castaldo","year":"2021","journal-title":"J. Med. Internet Res."},{"key":"10.1016\/j.prp.2025.156083_bib92","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/RBME.2020.3013489","article-title":"Secure and robust machine learning for healthcare: a survey","volume":"14","author":"Qayyum","year":"2021","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"10.1016\/j.prp.2025.156083_bib93","doi-asserted-by":"crossref","first-page":"245","DOI":"10.4155\/fmc-2021-0243","article-title":"Machine learning and deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry","volume":"14","author":"Kumar","year":"2022","journal-title":"Future Med. Chem."},{"key":"10.1016\/j.prp.2025.156083_bib94","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1002\/cpt.3159","article-title":"Using machine learning to individualize treatment effect estimation: challenges and opportunities","volume":"115","author":"Curth","year":"2024","journal-title":"Clin. Pharm. Ther."},{"key":"10.1016\/j.prp.2025.156083_bib95","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2023.102589","article-title":"Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer: a systematic review of epigenome-wide studies","volume":"143","author":"Yuan","year":"2023","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.prp.2025.156083_bib96","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1038\/s41588-025-02124-2","article-title":"Causal machine learning for single-cell genomics","volume":"57","author":"Tejada-Lapuerta","year":"2025","journal-title":"Nat. Genet."},{"key":"10.1016\/j.prp.2025.156083_bib97","doi-asserted-by":"crossref","DOI":"10.1093\/bfgp\/elae044","article-title":"Application of computational algorithms for single-cell RNA-seq and ATAC-seq in neurodegenerative diseases","volume":"24","author":"Choi","year":"2025","journal-title":"Brief. Funct. Genom."},{"key":"10.1016\/j.prp.2025.156083_bib98","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1038\/s41588-025-02157-7","article-title":"Circular RNA discovery with emerging sequencing and deep learning technologies","volume":"57","author":"Zhang","year":"2025","journal-title":"Nat. Genet."},{"key":"10.1016\/j.prp.2025.156083_bib99","doi-asserted-by":"crossref","first-page":"481","DOI":"10.4103\/ijc.IJC_399_20","article-title":"Artificial intelligence in cancer diagnostics and therapy: current perspectives","volume":"58","author":"Majumder","year":"2021","journal-title":"Indian J. Cancer"},{"key":"10.1016\/j.prp.2025.156083_bib100","doi-asserted-by":"crossref","first-page":"20220934","DOI":"10.1259\/bjr.20220934","article-title":"AI and machine learning ethics, law, diversity, and global impact","volume":"96","author":"Drabiak","year":"2023","journal-title":"Br. J. Radio."}],"container-title":["Pathology - Research and Practice"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0344033825002766?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0344033825002766?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T08:27:24Z","timestamp":1778747244000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0344033825002766"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8]]},"references-count":100,"alternative-id":["S0344033825002766"],"URL":"https:\/\/doi.org\/10.1016\/j.prp.2025.156083","relation":{},"ISSN":["0344-0338"],"issn-type":[{"value":"0344-0338","type":"print"}],"subject":[],"published":{"date-parts":[[2025,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Integration of machine learning in biomarker discovery for esophageal squamous cell carcinoma: Applications and future directions","name":"articletitle","label":"Article Title"},{"value":"Pathology - Research and Practice","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.prp.2025.156083","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier GmbH. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"156083"}}