{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T01:06:04Z","timestamp":1785546364335,"version":"3.56.0"},"reference-count":42,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computational Biology and Chemistry"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.compbiolchem.2026.109225","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T09:52:49Z","timestamp":1783677169000},"page":"109225","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P2","title":["Biomarkers for minimally invasive multigrade liver cancer diagnosis using integrative heterogeneous models and evolutionary algorithms"],"prefix":"10.1016","volume":"124","author":[{"given":"Rakesh Chandra","family":"Joshi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pallavi","family":"Srivastava","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rashmi","family":"Mishra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3620-6807","authenticated-orcid":false,"given":"Petr","family":"Cika","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Radim","family":"Burget","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2462-737X","authenticated-orcid":false,"given":"Malay Kishore","family":"Dutta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.compbiolchem.2026.109225_bib1","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1080\/14737140.2025.2467184","article-title":"Persisting challenges in the early detection of hepatocellular carcinoma","volume":"25","author":"Arvind","year":"2025","journal-title":"Expert. Rev. Anticancer. Ther."},{"issue":"2","key":"10.1016\/j.compbiolchem.2026.109225_bib2","doi-asserted-by":"crossref","DOI":"10.1177\/18758592241304994","article-title":"Bioinformatics screened of biomarkers for the prognosis of hepatocellular carcinoma","volume":"42","author":"Bao","year":"2025","journal-title":"Cancer Biomark."},{"issue":"9","key":"10.1016\/j.compbiolchem.2026.109225_bib3","doi-asserted-by":"crossref","first-page":"767","DOI":"10.3390\/e26090767","article-title":"Optimizing Prognostic Predictions in Liver Cancer with Machine Learning and Survival Analysis","volume":"26","author":"Cai","year":"2024","journal-title":"Entropy"},{"issue":"1","key":"10.1016\/j.compbiolchem.2026.109225_bib4","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1186\/s12943-024-02101-z","article-title":"Biomarkers for diagnosis and therapeutic options in hepatocellular carcinoma","volume":"23","author":"Chan","year":"2024","journal-title":"Mol. Cancer"},{"issue":"21","key":"10.1016\/j.compbiolchem.2026.109225_bib5","doi-asserted-by":"crossref","first-page":"10004","DOI":"10.3390\/app112110004","article-title":"Machine Learning Based on Resampling Approaches and Deep Reinforcement Learning for Credit Card Fraud Detection Systems","volume":"11","author":"Dang","year":"2021","journal-title":"Appl. Sci."},{"issue":"1\u20132","key":"10.1016\/j.compbiolchem.2026.109225_bib6","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.cca.2008.05.010","article-title":"Diagnosing and monitoring hepatocellular carcinoma with alpha-fetoprotein: New aspects and applications","volume":"395","author":"Debruyne","year":"2008","journal-title":"Clin. Chim. Acta"},{"issue":"1","key":"10.1016\/j.compbiolchem.2026.109225_bib7","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1186\/s12864-023-09194-8","article-title":"Combining a machine-learning derived 4-lncRNA signature with AFP and TNM stages in predicting early recurrence of hepatocellular carcinoma","volume":"24","author":"Fu","year":"2023","journal-title":"BMC Genom."},{"issue":"22","key":"10.1016\/j.compbiolchem.2026.109225_bib8","doi-asserted-by":"crossref","first-page":"15693","DOI":"10.1007\/s00521-021-06189-y","article-title":"SMOTE-NC and gradient boosting imputation based random forest classifier for predicting severity level of covid-19 patients with blood samples","volume":"33","author":"G\u00f6k","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.compbiolchem.2026.109225_bib9","doi-asserted-by":"crossref","first-page":"s84","DOI":"10.1002\/hep.1840360711","article-title":"Screening tests for hepatocellular carcinoma in patients with chronic hepatitis C: A systematic review","volume":"36","author":"Gebo","year":"2002","journal-title":"Hepatology"},{"key":"10.1016\/j.compbiolchem.2026.109225_bib10","doi-asserted-by":"crossref","DOI":"10.14710\/jtsiskom.8.2.2020.89-93","article-title":"K-means-SMOTE for handling class imbalance in the classification of diabetes with C4.5, SVM, and naive Bayes","author":"Hairani","year":"2020","journal-title":"J. Teknol. Dan. Sist. Komput."},{"issue":"2","key":"10.1016\/j.compbiolchem.2026.109225_bib11","doi-asserted-by":"crossref","first-page":"417","DOI":"10.3390\/appliedmath3020022","article-title":"Machine-Learning Classification Models to Predict Liver Cancer with Explainable AI to Discover Associated Genes","volume":"3","author":"Hasan","year":"2023","journal-title":"AppliedMath"},{"key":"10.1016\/j.compbiolchem.2026.109225_bib12","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2021.101894","article-title":"An imageomics and multi-network based deep learning model for risk assessment of liver transplantation for hepatocellular cancer","author":"He","year":"2021","journal-title":"Comput. Med. Imaging Graph."},{"issue":"1\u20132","key":"10.1016\/j.compbiolchem.2026.109225_bib13","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.mce.2010.07.014","article-title":"Does hCG or hCG\u03b2 play a role in cancer cell biology?","volume":"329","author":"Iles","year":"2010","journal-title":"Mol. Cell. Endocrinol."},{"issue":"10","key":"10.1016\/j.compbiolchem.2026.109225_bib14","doi-asserted-by":"crossref","first-page":"6896","DOI":"10.1007\/s00330-024-10671-1","article-title":"MRI imaging features for predicting macrotrabecular-massive subtype hepatocellular carcinoma: a systematic review and meta-analysis","volume":"34","author":"Kim","year":"2024","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.compbiolchem.2026.109225_bib15","doi-asserted-by":"crossref","DOI":"10.1016\/j.csbj.2014.11.005","article-title":"Machine learning applications in cancer prognosis and prediction","author":"Kourou","year":"2015","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"10.1016\/j.compbiolchem.2026.109225_bib16","doi-asserted-by":"crossref","DOI":"10.1002\/1878-0261.12809","article-title":"Cancer diagnostics based on plasma protein biomarkers: hard times but great expectations","author":"Landegren","year":"2021","journal-title":"Mol. Oncol."},{"key":"10.1016\/j.compbiolchem.2026.109225_bib17","first-page":"1","article-title":"CT Image Segmentation Method of Liver Tumor Based on Artificial Intelligence Enabled Medical Imaging","volume":"2021","author":"Liu","year":"2021","journal-title":"Math. Probl. Eng."},{"issue":"19","key":"10.1016\/j.compbiolchem.2026.109225_bib18","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2024.e38444","article-title":"Point-of-care testing for early-stage liver cancer diagnosis and personalized medicine: Biomarkers, current technologies and perspectives","volume":"10","author":"Liu","year":"2024","journal-title":"Heliyon"},{"issue":"4","key":"10.1016\/j.compbiolchem.2026.109225_bib19","doi-asserted-by":"crossref","first-page":"790","DOI":"10.3390\/cancers13040790","article-title":"Performance of Ultrasound Techniques and the Potential of Artificial Intelligence in the Evaluation of Hepatocellular Carcinoma and Non-Alcoholic Fatty Liver Disease","volume":"13","author":"Lupsor-Platon","year":"2021","journal-title":"Cancers"},{"issue":"1","key":"10.1016\/j.compbiolchem.2026.109225_bib20","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1186\/s12885-024-12109-9","article-title":"Research on multi-model imaging machine learning for distinguishing early hepatocellular carcinoma","volume":"24","author":"Ma","year":"2024","journal-title":"BMC Cancer"},{"issue":"12","key":"10.1016\/j.compbiolchem.2026.109225_bib21","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1038\/s41575-024-00967-4","article-title":"Liver diseases and hepatocellular carcinoma in the Asia-Pacific region: burden, trends, challenges and future directions","volume":"21","author":"Mak","year":"2024","journal-title":"Nat. Rev. Gastroenterol. & Hepatol."},{"key":"10.1016\/j.compbiolchem.2026.109225_bib22","first-page":"S29","article-title":"Assessment of liver cancer biomarkers","volume":"13","author":"Mansouri","year":"2020","journal-title":"Gastroenterol. Hepatol. Bed Bench"},{"issue":"1","key":"10.1016\/j.compbiolchem.2026.109225_bib23","doi-asserted-by":"crossref","first-page":"46","DOI":"10.3390\/diagnostics15010046","article-title":"Challenges in Diagnosing Focal Liver Lesions Using Contrast-Enhanced Ultrasound","volume":"15","author":"Moga","year":"2024","journal-title":"Diagnostics"},{"issue":"2","key":"10.1016\/j.compbiolchem.2026.109225_bib24","doi-asserted-by":"crossref","first-page":"159","DOI":"10.3390\/biomedicines9020159","article-title":"Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine","volume":"9","author":"Moldogazieva","year":"2021","journal-title":"Biomedicines"},{"issue":"1","key":"10.1016\/j.compbiolchem.2026.109225_bib25","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1186\/s12876-022-02182-4","article-title":"Machine learning-based survival rate prediction of Korean hepatocellular carcinoma patients using multi-center data","volume":"22","author":"Noh","year":"2022","journal-title":"BMC Gastroenterol."},{"issue":"1","key":"10.1016\/j.compbiolchem.2026.109225_bib26","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1007\/s11901-023-00626-3","article-title":"Novel Blood-Based Biomarkers for HCC","volume":"23","author":"Pai","year":"2024","journal-title":"Curr. Hepatol. Rep."},{"key":"10.1016\/j.compbiolchem.2026.109225_bib27","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2024.1355454","article-title":"Artificial intelligence in liver cancer research: a scientometrics analysis of trends and topics","volume":"14","author":"Rezaee-Zavareh","year":"2024","journal-title":"Front. Oncol."},{"issue":"9","key":"10.1016\/j.compbiolchem.2026.109225_bib28","doi-asserted-by":"crossref","DOI":"10.1515\/CCLM.2007.262","article-title":"New perspectives and strategy research biomarkers for hepatocellular carcinoma","volume":"45","author":"Saffroy","year":"2007","journal-title":"Clin. Chem. Lab. Med."},{"issue":"2","key":"10.1016\/j.compbiolchem.2026.109225_bib29","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1038\/s41379-020-00671-z","article-title":"Prediction of early recurrence of hepatocellular carcinoma after resection using digital pathology images assessed by machine learning","volume":"34","author":"Saito","year":"2021","journal-title":"Mod. Pathol."},{"key":"10.1016\/j.compbiolchem.2026.109225_bib30","doi-asserted-by":"crossref","DOI":"10.1080\/088800101750059828","article-title":"Diagnostic value of alpha1-fetoprotein and beta-human chorionic gonadotropin in infancy and childhood","author":"Schneider","year":"2001","journal-title":"Pediatr. Hematol. Oncol."},{"issue":"8","key":"10.1016\/j.compbiolchem.2026.109225_bib31","doi-asserted-by":"crossref","first-page":"3660","DOI":"10.1007\/s00261-021-03056-1","article-title":"Artificial intelligence in assessment of hepatocellular carcinoma treatment response","volume":"46","author":"Spieler","year":"2021","journal-title":"Abdom. Radiol."},{"issue":"13","key":"10.1016\/j.compbiolchem.2026.109225_bib32","doi-asserted-by":"crossref","first-page":"4751","DOI":"10.3390\/en15134751","article-title":"Borderline SMOTE Algorithm and Feature Selection-Based Network Anomalies Detection Strategy","volume":"15","author":"Sun","year":"2022","journal-title":"Energies"},{"issue":"3","key":"10.1016\/j.compbiolchem.2026.109225_bib33","first-page":"209","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA A Cancer Journal Clinicians"},{"issue":"4","key":"10.1016\/j.compbiolchem.2026.109225_bib34","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1158\/1055-9965.EPI-10-0069","article-title":"Urine Collection and Processing for Protein Biomarker Discovery and Quantification","volume":"19","author":"Thomas","year":"2010","journal-title":"Cancer Epidemiol. Biomark. & Prev."},{"issue":"1","key":"10.1016\/j.compbiolchem.2026.109225_bib35","doi-asserted-by":"crossref","first-page":"4737","DOI":"10.1038\/s41598-023-32027-3","article-title":"An improved hyperparameter optimization framework for AutoML systems using evolutionary algorithms","volume":"13","author":"Vincent","year":"2023","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.compbiolchem.2026.109225_bib36","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1038\/s41698-025-01010-8","article-title":"Consensus artificial intelligence-driven prognostic signature for predicting the prognosis of hepatocellular carcinoma: a multi-center and large-scale study","volume":"9","author":"Wen","year":"2025","journal-title":"npj Precis. Oncol."},{"issue":"3","key":"10.1016\/j.compbiolchem.2026.109225_bib37","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1111\/jgh.15385","article-title":"Artificial intelligence in prediction of non-alcoholic fatty liver disease and fibrosis","volume":"36","author":"Wong","year":"2021","journal-title":"J. Gastroenterol. Hepatol."},{"issue":"1","key":"10.1016\/j.compbiolchem.2026.109225_bib38","doi-asserted-by":"crossref","first-page":"54","DOI":"10.3390\/info14010054","article-title":"A Comparison of Undersampling, Oversampling, and SMOTE Methods for Dealing with Imbalanced Classification in Educational Data Mining","volume":"14","author":"Wongvorachan","year":"2023","journal-title":"Information"},{"issue":"1","key":"10.1016\/j.compbiolchem.2026.109225_bib39","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1186\/s12876-025-03697-2","article-title":"Development and validation of an interpretable machine learning model for predicting the risk of hepatocellular carcinoma in patients with chronic hepatitis B: a case-control study","volume":"25","author":"Wu","year":"2025","journal-title":"BMC Gastroenterol."},{"issue":"2","key":"10.1016\/j.compbiolchem.2026.109225_bib40","first-page":"41","article-title":"Identifying Possible Biomarkers for Early-Stage Hepatocellular Carcinoma using Random Forest Machine Learning Method","volume":"8","author":"Ya\u015far","year":"2024","journal-title":"J. Cogn. Syst."},{"key":"10.1016\/j.compbiolchem.2026.109225_bib41","article-title":"Application and progress of the detection technologies in hepatocellular carcinoma","author":"Yan","year":"2023","journal-title":"Genes. Dis."},{"issue":"7","key":"10.1016\/j.compbiolchem.2026.109225_bib42","doi-asserted-by":"crossref","first-page":"1262","DOI":"10.1016\/j.ajpath.2024.02.017","article-title":"Serum Fusion Transcripts to Assess the Risk of Hepatocellular Carcinoma and the Impact of Cancer Treatment through Machine Learning","volume":"194","author":"Yu","year":"2024","journal-title":"Am. J. Pathol."}],"container-title":["Computational Biology and Chemistry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S147692712600352X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S147692712600352X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T23:34:07Z","timestamp":1785540847000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S147692712600352X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":42,"alternative-id":["S147692712600352X"],"URL":"https:\/\/doi.org\/10.1016\/j.compbiolchem.2026.109225","relation":{},"ISSN":["1476-9271"],"issn-type":[{"value":"1476-9271","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Biomarkers for minimally invasive multigrade liver cancer diagnosis using integrative heterogeneous models and evolutionary algorithms","name":"articletitle","label":"Article Title"},{"value":"Computational Biology and Chemistry","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compbiolchem.2026.109225","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"109225"}}