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Cited by in F6Publishing
For: Wei Z, Sakamuru S, Zhang L, Zhao J, Huang R, Kleinstreuer NC, Chen Y, Shu Y, Knudsen TB, Xia M. Identification and Profiling of Environmental Chemicals That Inhibit the TGFβ/SMAD Signaling Pathway. Chem Res Toxicol 2019;32:2433-44. [PMID: 31652400 DOI: 10.1021/acs.chemrestox.9b00228] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
Number Citing Articles
1 Zou ML, Chen ZH, Teng YY, Liu SY, Jia Y, Zhang KW, Sun ZL, Wu JJ, Yuan ZD, Feng Y, Li X, Xu RS, Yuan FL. The Smad Dependent TGF-β and BMP Signaling Pathway in Bone Remodeling and Therapies. Front Mol Biosci 2021;8:593310. [PMID: 34026818 DOI: 10.3389/fmolb.2021.593310] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
2 Ciallella HL, Russo DP, Sharma S, Li Y, Sloter E, Sweet L, Huang H, Zhu H. Predicting Prenatal Developmental Toxicity Based On the Combination of Chemical Structures and Biological Data. Environ Sci Technol 2022;56:5984-98. [PMID: 35451820 DOI: 10.1021/acs.est.2c01040] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Sakamuru S, Huang R, Xia M. Use of Tox21 Screening Data to Evaluate the COVID-19 Drug Candidates for Their Potential Toxic Effects and Related Pathways. Front Pharmacol 2022;13:935399. [DOI: 10.3389/fphar.2022.935399] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Matsuzaka Y, Uesawa Y. Molecular Image-Based Prediction Models of Nuclear Receptor Agonists and Antagonists Using the DeepSnap-Deep Learning Approach with the Tox21 10K Library. Molecules 2020;25:E2764. [PMID: 32549344 DOI: 10.3390/molecules25122764] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 2.5] [Reference Citation Analysis]