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Cited by in F6Publishing
For: Goh WWB, Zhao Y, Sue AC, Guo T, Wong L. Proteomic investigation of intra-tumor heterogeneity using network-based contextualization - A case study on prostate cancer. J Proteomics 2019;206:103446. [PMID: 31323421 DOI: 10.1016/j.jprot.2019.103446] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
Number Citing Articles
1 Sadeesh N, Scaravilli M, Latonen L. Proteomic Landscape of Prostate Cancer: The View Provided by Quantitative Proteomics, Integrative Analyses, and Protein Interactomes. Cancers (Basel) 2021;13:4829. [PMID: 34638309 DOI: 10.3390/cancers13194829] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
2 Tonry C, Finn S, Armstrong J, Pennington SR. Clinical proteomics for prostate cancer: understanding prostate cancer pathology and protein biomarkers for improved disease management. Clin Proteomics 2020;17:41. [PMID: 33292167 DOI: 10.1186/s12014-020-09305-7] [Cited by in Crossref: 1] [Cited by in F6Publishing: 6] [Article Influence: 0.5] [Reference Citation Analysis]
3 Ho SY, Wong L, Goh WWB. Avoid Oversimplifications in Machine Learning: Going beyond the Class-Prediction Accuracy. Patterns (N Y) 2020;1:100025. [PMID: 33205097 DOI: 10.1016/j.patter.2020.100025] [Cited by in Crossref: 4] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]