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For: Bloomingdale P, Nguyen VA, Niu J, Mager DE. Boolean network modeling in systems pharmacology. J Pharmacokinet Pharmacodyn. 2018;45:159-180. [PMID: 29307099 DOI: 10.1007/s10928-017-9567-4] [Cited by in Crossref: 31] [Cited by in F6Publishing: 20] [Article Influence: 7.8] [Reference Citation Analysis]
Number Citing Articles
1 Putnins M, Campagne O, Mager DE, Androulakis IP. From data to QSP models: a pipeline for using Boolean networks for hypothesis inference and dynamic model building. J Pharmacokinet Pharmacodyn 2022. [PMID: 34988912 DOI: 10.1007/s10928-021-09797-2] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Recanatini M, Cabrelle C. Drug Research Meets Network Science: Where Are We? J Med Chem 2020;63:8653-66. [PMID: 32338900 DOI: 10.1021/acs.jmedchem.9b01989] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
3 Bloomingdale P, Meregalli C, Pollard K, Canta A, Chiorazzi A, Fumagalli G, Monza L, Pozzi E, Alberti P, Ballarini E, Oggioni N, Carlson L, Liu W, Ghandili M, Ignatowski TA, Lee KP, Moore MJ, Cavaletti G, Mager DE. Systems Pharmacology Modeling Identifies a Novel Treatment Strategy for Bortezomib-Induced Neuropathic Pain. Front Pharmacol 2022;12:817236. [DOI: 10.3389/fphar.2021.817236] [Reference Citation Analysis]
4 Qi Y, Zhou N, Jiang Q, Wang Z, Zhang Y, Li B, Xu W, Liu J, Wang Z, Zhu L. Dose-Dependent Variation of Synchronous Metabolites and Modules in a Yin/Yang Transformation Model of Appointed Ischemia Metabolic Networks. Front Neurosci 2021;15:645185. [PMID: 34531713 DOI: 10.3389/fnins.2021.645185] [Reference Citation Analysis]
5 Campbell C, Albert R. Edgetic perturbations to eliminate fixed-point attractors in Boolean regulatory networks. Chaos 2019;29:023130. [PMID: 30823730 DOI: 10.1063/1.5083060] [Cited by in Crossref: 4] [Article Influence: 1.3] [Reference Citation Analysis]
6 Niu J, Straubinger RM, Mager DE. Pharmacodynamic Drug-Drug Interactions. Clin Pharmacol Ther 2019;105:1395-406. [PMID: 30912119 DOI: 10.1002/cpt.1434] [Cited by in Crossref: 18] [Cited by in F6Publishing: 16] [Article Influence: 6.0] [Reference Citation Analysis]
7 McComb M, Bies R, Ramanathan M. Machine learning in pharmacometrics: Opportunities and challenges. Br J Clin Pharmacol 2021. [PMID: 33634893 DOI: 10.1111/bcp.14801] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
8 Hou W, Ruan P, Ching WK, Akutsu T. On the number of driver nodes for controlling a Boolean network when the targets are restricted to attractors. J Theor Biol 2019;463:1-11. [PMID: 30543810 DOI: 10.1016/j.jtbi.2018.12.012] [Cited by in Crossref: 2] [Article Influence: 0.5] [Reference Citation Analysis]
9 Nanavati C, Mager DE. Network-Based Systems Analysis Explains Sequence-Dependent Synergism of Bortezomib and Vorinostat in Multiple Myeloma. AAPS J 2021;23:101. [PMID: 34403034 DOI: 10.1208/s12248-021-00622-9] [Reference Citation Analysis]
10 Rabajante JF, Del Rosario RCH. Modeling Long ncRNA-Mediated Regulation in the Mammalian Cell Cycle. Methods Mol Biol 2019;1912:427-45. [PMID: 30635904 DOI: 10.1007/978-1-4939-8982-9_17] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
11 Niu J, Nguyen VA, Ghasemi M, Chen T, Mager DE. Cluster Gauss-Newton and CellNOpt Parameter Estimation in a Small Protein Signaling Network of Vorinostat and Bortezomib Pharmacodynamics. AAPS J 2021;23:110. [PMID: 34622346 DOI: 10.1208/s12248-021-00640-7] [Reference Citation Analysis]
12 Piñero J, Furlong LI, Sanz F. In silico models in drug development: where we are. Curr Opin Pharmacol 2018;42:111-21. [PMID: 30205360 DOI: 10.1016/j.coph.2018.08.007] [Cited by in Crossref: 14] [Cited by in F6Publishing: 12] [Article Influence: 3.5] [Reference Citation Analysis]
13 Sinisi S, Alimguzhin V, Mancini T, Tronci E, Leeners B. Complete populations of virtual patients for in silico clinical trials. Bioinformatics 2020:btaa1026. [PMID: 33325489 DOI: 10.1093/bioinformatics/btaa1026] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
14 Aghamiri SS, Amin R, Helikar T. Recent applications of quantitative systems pharmacology and machine learning models across diseases. J Pharmacokinet Pharmacodyn 2021. [PMID: 34671863 DOI: 10.1007/s10928-021-09790-9] [Reference Citation Analysis]
15 Yang G, Gómez Tejeda Zañudo J, Albert R. Target Control in Logical Models Using the Domain of Influence of Nodes. Front Physiol 2018;9:454. [PMID: 29867523 DOI: 10.3389/fphys.2018.00454] [Cited by in Crossref: 14] [Cited by in F6Publishing: 8] [Article Influence: 3.5] [Reference Citation Analysis]
16 G T Zañudo J, Steinway SN, Albert R. Discrete dynamic network modeling of oncogenic signaling: Mechanistic insights for personalized treatment of cancer. Curr Opin Syst Biol 2018;9:1-10. [PMID: 32954058 DOI: 10.1016/j.coisb.2018.02.002] [Cited by in Crossref: 26] [Cited by in F6Publishing: 10] [Article Influence: 6.5] [Reference Citation Analysis]
17 Marku M, Verstraete N, Raynal F, Madrid-Mencía M, Domagala M, Fournié JJ, Ysebaert L, Poupot M, Pancaldi V. Insights on TAM Formation from a Boolean Model of Macrophage Polarization Based on In Vitro Studies. Cancers (Basel) 2020;12:E3664. [PMID: 33297362 DOI: 10.3390/cancers12123664] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
18 Azer K, Kaddi CD, Barrett JS, Bai JPF, McQuade ST, Merrill NJ, Piccoli B, Neves-Zaph S, Marchetti L, Lombardo R, Parolo S, Immanuel SRC, Baliga NS. History and Future Perspectives on the Discipline of Quantitative Systems Pharmacology Modeling and Its Applications. Front Physiol 2021;12:637999. [PMID: 33841175 DOI: 10.3389/fphys.2021.637999] [Cited by in Crossref: 1] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
19 Bloomingdale P, Karelina T, Cirit M, Muldoon SF, Baker J, McCarty WJ, Geerts H, Macha S. Quantitative systems pharmacology in neuroscience: Novel methodologies and technologies. CPT Pharmacometrics Syst Pharmacol 2021;10:412-9. [PMID: 33719204 DOI: 10.1002/psp4.12607] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
20 Deritei D, Rozum J, Ravasz Regan E, Albert R. A feedback loop of conditionally stable circuits drives the cell cycle from checkpoint to checkpoint. Sci Rep 2019;9:16430. [PMID: 31712566 DOI: 10.1038/s41598-019-52725-1] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
21 Chen YL, Zhang YL, Dai YC, Tang ZP. Systems pharmacology approach reveals the antiinflammatory effects of Ampelopsis grossedentata on dextran sodium sulfate-induced colitis. World J Gastroenterol 2018; 24(13): 1398-1409 [PMID: 29632421 DOI: 10.3748/wjg.v24.i13.1398] [Cited by in CrossRef: 13] [Cited by in F6Publishing: 11] [Article Influence: 3.3] [Reference Citation Analysis]
22 Rajapakse VN, Herrada S, Lavi O. Phenotype stability under dynamic brain-tumor environment stimuli maps glioblastoma progression in patients. Sci Adv 2020;6:eaaz4125. [PMID: 32832595 DOI: 10.1126/sciadv.aaz4125] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]