{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T22:40:09Z","timestamp":1736376009176,"version":"3.32.0"},"reference-count":30,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"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":["Gene"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1016\/j.gene.2023.147467","type":"journal-article","created":{"date-parts":[[2023,5,9]],"date-time":"2023-05-09T00:58:51Z","timestamp":1683593931000},"page":"147467","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":3,"special_numbering":"C","title":["Discovering pathway biomarkers of hepatocellular carcinoma occurrence and development by dynamic network entropy analysis"],"prefix":"10.1016","volume":"873","author":[{"given":"Chen","family":"Shen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4590-994X","authenticated-orcid":false,"given":"Yi","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoqiang","family":"Qi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7742-9161","authenticated-orcid":false,"given":"Zhi-Ping","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.gene.2023.147467_b0005","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1136\/jclinpath-2018-205611","article-title":"Stromal morphological changes and immunophenotypic features of precancerous lesions and hepatocellular carcinoma","volume":"72","author":"Chen","year":"2019","journal-title":"J. Clin. Pathol."},{"key":"10.1016\/j.gene.2023.147467_b0010","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41588-020-00774-y","article-title":"Comprehensive characterization of protein\u2013protein interactions perturbed by disease mutations","volume":"53","author":"Cheng","year":"2021","journal-title":"Nat. Genet."},{"key":"10.1016\/j.gene.2023.147467_b0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.biosystems.2022.104792","article-title":"Computational intelligence and machine learning in bioinformatics and computational biology","volume":"222","author":"Chetty","year":"2022","journal-title":"Biosystems"},{"key":"10.1016\/j.gene.2023.147467_b0020","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1093\/bioinformatics\/btl056","article-title":"maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments","volume":"22","author":"Conesa","year":"2006","journal-title":"Bioinformatics"},{"key":"10.1016\/j.gene.2023.147467_b0025","article-title":"Serum miRNA are promising biomarkers for the detection of early hepatocellular carcinoma after treatment with direct-acting antivirals","volume":"11, 1773","author":"Devis","year":"2019","journal-title":"Cancers"},{"key":"10.1016\/j.gene.2023.147467_b0030","doi-asserted-by":"crossref","first-page":"5759","DOI":"10.3748\/wjg.v26.i38.5759","article-title":"Molecular mechanisms of viral hepatitis induced hepatocellular carcinoma","volume":"26","author":"D'Souza","year":"2020","journal-title":"World J. Gastroenterol."},{"key":"10.1016\/j.gene.2023.147467_b0035","article-title":"Genome-wide high resolution expression map and functions of key cell fate determinants reveal the dynamics of crown root development in rice","author":"Garg","year":"2021","journal-title":"BioRxiv."},{"key":"10.1016\/j.gene.2023.147467_b0040","first-page":"9, 325","article-title":"Machine learning model for identifying antioxidant proteins using features calculated from primary sequences","author":"Ho Thanh Lam","year":"2020","journal-title":"Biology (Basel)"},{"key":"10.1016\/j.gene.2023.147467_b0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.humimm.2021.02.012","article-title":"Next-generation sequencing technologies: An overview","author":"Hu","year":"2021","journal-title":"Hum. Immunol. 82, 801-811"},{"key":"10.1016\/j.gene.2023.147467_b0050","first-page":"51, 1144","article-title":"Development of web-based nomograms to predict treatment response and prognosis of epithelial ovarian cancer","author":"Kim","year":"2018","journal-title":"Cancer Res. Treat."},{"key":"10.1016\/j.gene.2023.147467_b0055","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1208\/s12248-021-00644-3","article-title":"Machine learning and artificial intelligence in pharmaceutical research and development: a review","volume":"24","author":"Kolluri","year":"2022","journal-title":"AAPS J."},{"key":"10.1016\/j.gene.2023.147467_b0060","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1038\/nmeth.4083","article-title":"A scored human protein-protein interaction network to catalyze genomic interpretation","volume":"14","author":"Li","year":"2017","journal-title":"Nat. Methods."},{"key":"10.1016\/j.gene.2023.147467_b0065","doi-asserted-by":"crossref","first-page":"633","DOI":"10.2217\/bmm-2015-0035","article-title":"Identifying network-based biomarkers of complex diseases from high-throughput data","author":"Liu","year":"2016","journal-title":"Biomark. Med."},{"key":"10.1016\/j.gene.2023.147467_b0070","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.jbi.2018.05.006","article-title":"Detecting pathway biomarkers of diabetic progression with differential entropy","volume":"82","author":"Liu","year":"2018","journal-title":"J. Biomed. Inform."},{"key":"10.1016\/j.gene.2023.147467_b0075","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1139\/gen-2020-0131","article-title":"Machine learning for precision medicine","volume":"64","author":"MacEachern","year":"2021","journal-title":"Genome"},{"key":"10.1016\/j.gene.2023.147467_b0080","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.euros.2021.07.006","article-title":"Prediction of metastatic patterns in bladder cancer: Spatiotemporal progression and development of a novel, web-based platform for clinical utility","volume":"32","author":"Mason","year":"2021","journal-title":"Eur. Urol. Open Sci."},{"key":"10.1016\/j.gene.2023.147467_b0085","doi-asserted-by":"crossref","first-page":"461 -","DOI":"10.1186\/1471-2105-9-461","article-title":"minet: A R\/Bioconductor package for inferring large transcriptional networks using mutual information","volume":"9","author":"Meyer","year":"2008","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.gene.2023.147467_b0090","article-title":"A Survey on Prediction of Protein-Protein Interactions","volume":"137","author":"Nivedha","year":"2021","journal-title":"J. Phys.: Conf. Series."},{"key":"10.1016\/j.gene.2023.147467_b0095","doi-asserted-by":"crossref","first-page":"bbac451","DOI":"10.1093\/bib\/bbac451","article-title":"Identification of potential driver mutations in glioblastoma using machine learning","volume":"23","author":"Pandey","year":"2022","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.gene.2023.147467_b0100","doi-asserted-by":"crossref","first-page":"391","DOI":"10.3390\/e21040391","article-title":"New developments in statistical information theory based on entropy and divergence measures","volume":"21","author":"Pardo","year":"2019","journal-title":"Entropy"},{"key":"10.1016\/j.gene.2023.147467_b0105","doi-asserted-by":"crossref","first-page":"S361","DOI":"10.1016\/S1590-8658(11)60592-6","article-title":"Liver precancerous lesions and hepatocellular carcinoma: The histology report","volume":"43","author":"Roncalli","year":"2011","journal-title":"Dig. Liver Dis."},{"key":"10.1016\/j.gene.2023.147467_b0110","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/0046-8177(91)90039-R","article-title":"Early stages of multistep hepatocarcinogenesis: adenomatous hyperplasia and early hepatocellular carcinoma","volume":"22","author":"Sakamoto","year":"1991","journal-title":"Hum. Pathol."},{"key":"10.1016\/j.gene.2023.147467_b0115","doi-asserted-by":"crossref","first-page":"204","DOI":"10.3171\/2012.4.JNS101702","article-title":"Extent of tumor removal and molecular markers in cerebral glioblastoma: a combined prognostic factors study in a surgical series of 105 patients","volume":"117","author":"Salvati","year":"2012","journal-title":"J. Neurosurg."},{"key":"10.1016\/j.gene.2023.147467_b0120","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A mathematical theory of communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"10.1016\/j.gene.2023.147467_b0125","doi-asserted-by":"crossref","first-page":"1360","DOI":"10.1002\/hep.29606","article-title":"Barrier to autointegration factor 1, procollagen-lysine, 2-oxoglutarate 5-dioxygenase 3, and splicing factor 3b subunit 4 as early-stage cancer decision markers and drivers of hepatocellular carcinoma","volume":"67","author":"Shen","year":"2018","journal-title":"Hepatology"},{"key":"10.1016\/j.gene.2023.147467_b0130","first-page":"248","article-title":"Quantifying Waddington\u2019s epigenetic landscape: a comparison of single-cell potency measures","volume":"21","author":"Shi","year":"2020","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.gene.2023.147467_b0135","doi-asserted-by":"crossref","first-page":"2112","DOI":"10.1016\/j.csbj.2022.04.021","article-title":"On the road to explainable AI in drug-drug interactions prediction: A systematic review","volume":"20","author":"Vo","year":"2022","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"10.1016\/j.gene.2023.147467_b0140","doi-asserted-by":"crossref","first-page":"938","DOI":"10.1002\/hep.21622","article-title":"Genome-wide molecular profiles of HCV-induced dysplasia and hepatocellular carcinoma","volume":"45","author":"Wurmbach","year":"2007","journal-title":"Hepatology"},{"key":"10.1016\/j.gene.2023.147467_b0145","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1186\/s13059-019-1689-0","article-title":"Machine learning and complex biological data","volume":"20","author":"Xu","year":"2019","journal-title":"Genome Biol."},{"key":"10.1016\/j.gene.2023.147467_b0150","doi-asserted-by":"crossref","DOI":"10.3389\/fninf.2021.596443","article-title":"A quick and easy way to estimate entropy and mutual information for neuroscience","volume":"15","author":"Zbili","year":"2021","journal-title":"Front. Neuroinform."}],"container-title":["Gene"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0378111923003086?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0378111923003086?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T22:23:25Z","timestamp":1736375005000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0378111923003086"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7]]},"references-count":30,"alternative-id":["S0378111923003086"],"URL":"https:\/\/doi.org\/10.1016\/j.gene.2023.147467","relation":{},"ISSN":["0378-1119"],"issn-type":[{"type":"print","value":"0378-1119"}],"subject":[],"published":{"date-parts":[[2023,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Discovering pathway biomarkers of hepatocellular carcinoma occurrence and development by dynamic network entropy analysis","name":"articletitle","label":"Article Title"},{"value":"Gene","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.gene.2023.147467","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"147467"}}