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Cited by in F6Publishing
For: Lin H, Qiu X, Zhang B, Zhang J. Identification of the predictive genes for the response of colorectal cancer patients to FOLFOX therapy. Onco Targets Ther 2018;11:5943-55. [PMID: 30271178 DOI: 10.2147/OTT.S167656] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
Number Citing Articles
1 Banegas-Luna AJ, Peña-García J, Iftene A, Guadagni F, Ferroni P, Scarpato N, Zanzotto FM, Bueno-Crespo A, Pérez-Sánchez H. Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey. Int J Mol Sci 2021;22:4394. [PMID: 33922356 DOI: 10.3390/ijms22094394] [Reference Citation Analysis]
2 He J, Cheng J, Guan Q, Yan H, Li Y, Zhao W, Guo Z, Wang X. Qualitative transcriptional signature for predicting pathological response of colorectal cancer to FOLFOX therapy. Cancer Sci 2020;111:253-65. [PMID: 31785020 DOI: 10.1111/cas.14263] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 1.0] [Reference Citation Analysis]
3 Wang Y, Zhao M, Zhao H, Cheng S, Bai R, Song M. MicroRNA-940 restricts the expression of metastasis-associated gene MACC1 and enhances the antitumor effect of Anlotinib on colorectal cancer. Onco Targets Ther 2019;12:2809-22. [PMID: 31114229 DOI: 10.2147/OTT.S195364] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]