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For: Shen J, Qi L, Zou Z, Du J, Kong W, Zhao L, Wei J, Lin L, Ren M, Liu B. Identification of a novel gene signature for the prediction of recurrence in HCC patients by machine learning of genome-wide databases. Sci Rep 2020;10:4435. [PMID: 32157118 DOI: 10.1038/s41598-020-61298-3] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
Number Citing Articles
1 Wang L, Wu M, Zhu C, Li R, Bao S, Yang S, Dong J. Ensemble learning based on efficient features combination can predict the outcome of recurrence-free survival in patients with hepatocellular carcinoma within three years after surgery. Front Oncol 2022;12. [DOI: 10.3389/fonc.2022.1019009] [Reference Citation Analysis]
2 Dessie EY, Gautam Y, Ding L, Altaye M, Beyene J, Mersha TB. Development and validation of asthma risk prediction models using co- expression gene modules and machine learning methods.. [DOI: 10.21203/rs.3.rs-2098680/v1] [Reference Citation Analysis]
3 Christou CD, Tsoulfas G. Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities. World J Gastrointest Oncol 2022; 14(4): 765-793 [DOI: 10.4251/wjgo.v14.i4.765] [Reference Citation Analysis]
4 Chamseddine I, Kim Y, De B, El Naqa I, Duda DG, Wolfgang J, Pursley J, Paganetti H, Wo J, Hong T, Koay EJ, Grassberger C. Predictive Modeling of Survival and Toxicity in Patients With Hepatocellular Carcinoma After Radiotherapy. JCO Clin Cancer Inform 2022;6:e2100169. [PMID: 35192402 DOI: 10.1200/CCI.21.00169] [Reference Citation Analysis]
5 Kwon YW, Jo HS, Bae S, Seo Y, Song P, Song M, Yoon JH. Application of Proteomics in Cancer: Recent Trends and Approaches for Biomarkers Discovery. Front Med (Lausanne) 2021;8:747333. [PMID: 34631760 DOI: 10.3389/fmed.2021.747333] [Cited by in Crossref: 14] [Cited by in F6Publishing: 16] [Article Influence: 14.0] [Reference Citation Analysis]
6 Balsano C, Alisi A, Brunetto MR, Invernizzi P, Burra P, Piscaglia F; Special Interest Group (SIG) Artificial Intelligence and Liver Diseases; Italian Association for the Study of the Liver (AISF). The application of artificial intelligence in hepatology: A systematic review. Dig Liver Dis 2021:S1590-8658(21)00317-0. [PMID: 34266794 DOI: 10.1016/j.dld.2021.06.011] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
7 Kunadirek P, Chuaypen N, Jenjaroenpun P, Wongsurawat T, Pinjaroen N, Sirichindakul P, Nookaew I, Tangkijvanich P. Cell-Free DNA Analysis by Whole-Exome Sequencing for Hepatocellular Carcinoma: A Pilot Study in Thailand. Cancers (Basel) 2021;13:2229. [PMID: 34066484 DOI: 10.3390/cancers13092229] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
8 Moldogazieva NT, Mokhosoev IM, Zavadskiy SP, Terentiev AA. Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine. Biomedicines 2021;9:159. [PMID: 33562077 DOI: 10.3390/biomedicines9020159] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 7.0] [Reference Citation Analysis]
9 Sorgini A, Kim HAJ, Zeng PYF, Shaikh MH, Mundi N, Ghasemi F, Di Gravio E, Khan H, MacNeil D, Khan MI, Mendez A, Yoo J, Fung K, Lang P, Palma DA, Mymryk JS, Barrett JW, Patel KB, Boutros PC, Nichols AC. Analysis of the TCGA Dataset Reveals that Subsites of Laryngeal Squamous Cell Carcinoma are Molecularly Distinct. Cancers (Basel) 2020;13:E105. [PMID: 33396315 DOI: 10.3390/cancers13010105] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 0.5] [Reference Citation Analysis]