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For: Wang Q, Vattai A, Vilsmaier T, Kaltofen T, Steger A, Mayr D, Mahner S, Jeschke U, Hildegard Heidegger H. Immunogenomic Identification for Predicting the Prognosis of Cervical Cancer Patients. Int J Mol Sci 2021;22:2442. [PMID: 33671013 DOI: 10.3390/ijms22052442] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 5.0] [Reference Citation Analysis]
Number Citing Articles
1 Chen C, Cao Y, Li W, Liu Z, Liu P, Tian X, Sun C, Wang W, Gao H, Kang S, Wang S, Jiang J, Chen C, Tian J. The pathological risk score: A new deep learning-based signature for predicting survival in cervical cancer. Cancer Med 2022. [PMID: 35762423 DOI: 10.1002/cam4.4953] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
2 杨 克. Research Progress of TFRC in Gynecological Tumors. ACM 2022;12:7047-7052. [DOI: 10.12677/acm.2022.1281014] [Reference Citation Analysis]
3 Korsensky L, Chorev D, Saleem H, Heller-Japheth R, Rabinovitz S, Haif S, Dahan N, Ziv T, Ron D. Regulation of stability and inhibitory activity of the tumor suppressor SEF through casein-kinase II-mediated phosphorylation. Cell Signal 2021;86:110085. [PMID: 34280495 DOI: 10.1016/j.cellsig.2021.110085] [Reference Citation Analysis]
4 Li L, Gao H, Wang D, Jiang H, Wang H, Yu J, Jiang X, Huang C. Metabolism-Relevant Molecular Classification Identifies Tumor Immune Microenvironment Characterization and Immunotherapeutic Effect in Cervical Cancer. Front Mol Biosci 2021;8:624951. [PMID: 34277697 DOI: 10.3389/fmolb.2021.624951] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
5 Ikrar T, Anurogo D. The Art of Oncoimmunovaccinomics. WJV 2021;11:50-66. [DOI: 10.4236/wjv.2021.114007] [Reference Citation Analysis]