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Cited by in F6Publishing
For: Sun X, Hu B. Mathematical modeling and computational prediction of cancer drug resistance. Brief Bioinform 2018;19:1382-99. [PMID: 28981626 DOI: 10.1093/bib/bbx065] [Cited by in Crossref: 40] [Cited by in F6Publishing: 27] [Article Influence: 13.3] [Reference Citation Analysis]
Number Citing Articles
1 Sahai N, Gogoi M, Ahmad N. Mathematical Modeling and Simulations for Developing Nanoparticle-Based Cancer Drug Delivery Systems: A Review. Curr Pathobiol Rep 2021;9:1-8. [DOI: 10.1007/s40139-020-00219-5] [Cited by in Crossref: 6] [Article Influence: 6.0] [Reference Citation Analysis]
2 Saini A, Gallo JM. Epigenetic instability may alter cell state transitions and anticancer drug resistance. PLoS Comput Biol 2021;17:e1009307. [PMID: 34424912 DOI: 10.1371/journal.pcbi.1009307] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
3 Sarhaddi M, Yaghoobi M. A new approach in cancer treatment regimen using adaptive fuzzy back-stepping sliding mode control and tumor-immunity fractional order model. Biocybernetics and Biomedical Engineering 2020;40:1654-65. [DOI: 10.1016/j.bbe.2020.09.003] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
4 Ward RA, Fawell S, Floc'h N, Flemington V, McKerrecher D, Smith PD. Challenges and Opportunities in Cancer Drug Resistance. Chem Rev 2021;121:3297-351. [PMID: 32692162 DOI: 10.1021/acs.chemrev.0c00383] [Cited by in Crossref: 23] [Cited by in F6Publishing: 19] [Article Influence: 11.5] [Reference Citation Analysis]
5 Álvarez-Arenas A, Podolski-Renic A, Belmonte-Beitia J, Pesic M, Calvo GF. Interplay of Darwinian Selection, Lamarckian Induction and Microvesicle Transfer on Drug Resistance in Cancer. Sci Rep 2019;9:9332. [PMID: 31249353 DOI: 10.1038/s41598-019-45863-z] [Cited by in Crossref: 18] [Cited by in F6Publishing: 12] [Article Influence: 6.0] [Reference Citation Analysis]
6 Gianì F, Russo G, Pennisi M, Sciacca L, Frasca F, Pappalardo F. Computational modeling reveals MAP3K8 as mediator of resistance to vemurafenib in thyroid cancer stem cells. Bioinformatics 2019;35:2267-75. [PMID: 30481266 DOI: 10.1093/bioinformatics/bty969] [Cited by in Crossref: 14] [Cited by in F6Publishing: 11] [Article Influence: 7.0] [Reference Citation Analysis]
7 Jong ED, Chan ICW, Nedelcu AM. A Model-System to Address the Impact of Phenotypic Heterogeneity and Plasticity on the Development of Cancer Therapies. Front Oncol 2019;9:842. [PMID: 31555595 DOI: 10.3389/fonc.2019.00842] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
8 Zhang J, Zhu W, Wang Q, Gu J, Huang LF, Sun X. Differential regulatory network-based quantification and prioritization of key genes underlying cancer drug resistance based on time-course RNA-seq data. PLoS Comput Biol 2019;15:e1007435. [PMID: 31682596 DOI: 10.1371/journal.pcbi.1007435] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 3.3] [Reference Citation Analysis]
9 Spolaor S, Scheve M, Firat M, Cazzaniga P, Besozzi D, Nobile MS. Screening for Combination Cancer Therapies With Dynamic Fuzzy Modeling and Multi-Objective Optimization. Front Genet 2021;12:617935. [PMID: 33868363 DOI: 10.3389/fgene.2021.617935] [Reference Citation Analysis]
10 Norton KA, Gong C, Jamalian S, Popel AS. Multiscale Agent-Based and Hybrid Modeling of the Tumor Immune Microenvironment. Processes (Basel) 2019;7:37. [PMID: 30701168 DOI: 10.3390/pr7010037] [Cited by in Crossref: 46] [Cited by in F6Publishing: 38] [Article Influence: 15.3] [Reference Citation Analysis]
11 Bao K. An elementary mathematical modeling of drug resistance in cancer. Math Biosci Eng 2020;18:339-53. [PMID: 33525095 DOI: 10.3934/mbe.2021018] [Reference Citation Analysis]
12 Friedman A, Siewe N. Overcoming Drug Resistance to BRAF Inhibitor. Bull Math Biol 2020;82:8. [PMID: 31933021 DOI: 10.1007/s11538-019-00691-0] [Reference Citation Analysis]
13 Lee S, Rauch J, Kolch W. Targeting MAPK Signaling in Cancer: Mechanisms of Drug Resistance and Sensitivity. Int J Mol Sci 2020;21:E1102. [PMID: 32046099 DOI: 10.3390/ijms21031102] [Cited by in Crossref: 68] [Cited by in F6Publishing: 76] [Article Influence: 34.0] [Reference Citation Analysis]
14 Ahmadpour S, Taghavi T, Sheida A, Tamehri Zadeh SS, Hamblin MR, Mirzaei H. Effects of microRNAs and long non-coding RNAs on chemotherapy response in glioma. Epigenomics 2022. [PMID: 35473299 DOI: 10.2217/epi-2021-0439] [Reference Citation Analysis]
15 Bekisz S, Geris L. Cancer modeling: From mechanistic to data-driven approaches, and from fundamental insights to clinical applications. Journal of Computational Science 2020;46:101198. [DOI: 10.1016/j.jocs.2020.101198] [Cited by in Crossref: 7] [Cited by in F6Publishing: 1] [Article Influence: 3.5] [Reference Citation Analysis]
16 Yin A, Moes DJAR, van Hasselt JGC, Swen JJ, Guchelaar HJ. A Review of Mathematical Models for Tumor Dynamics and Treatment Resistance Evolution of Solid Tumors. CPT Pharmacometrics Syst Pharmacol 2019;8:720-37. [PMID: 31250989 DOI: 10.1002/psp4.12450] [Cited by in Crossref: 18] [Cited by in F6Publishing: 9] [Article Influence: 6.0] [Reference Citation Analysis]
17 Cortesi M, Giordano E. Non-destructive monitoring of 3D cell cultures: new technologies and applications. PeerJ 2022;10:e13338. [DOI: 10.7717/peerj.13338] [Reference Citation Analysis]
18 Jarrett AM, Shah A, Bloom MJ, McKenna MT, Hormuth DA 2nd, Yankeelov TE, Sorace AG. Experimentally-driven mathematical modeling to improve combination targeted and cytotoxic therapy for HER2+ breast cancer. Sci Rep 2019;9:12830. [PMID: 31492947 DOI: 10.1038/s41598-019-49073-5] [Cited by in Crossref: 13] [Cited by in F6Publishing: 7] [Article Influence: 4.3] [Reference Citation Analysis]
19 Hong WS, Wang SG, Zhang GQ. Lung Cancer Radiotherapy: Simulation and Analysis Based on a Multicomponent Mathematical Model. Comput Math Methods Med 2021;2021:6640051. [PMID: 34012477 DOI: 10.1155/2021/6640051] [Reference Citation Analysis]
20 Radaeva M, Dong X, Cherkasov A. The Use of Methods of Computer-Aided Drug Discovery in the Development of Topoisomerase II Inhibitors: Applications and Future Directions. J Chem Inf Model 2020;60:3703-21. [DOI: 10.1021/acs.jcim.0c00325] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
21 Zhang J, Guan M, Wang Q, Zhang J, Zhou T, Sun X. Single-cell transcriptome-based multilayer network biomarker for predicting prognosis and therapeutic response of gliomas. Briefings in Bioinformatics 2020;21:1080-97. [DOI: 10.1093/bib/bbz040] [Cited by in Crossref: 23] [Cited by in F6Publishing: 24] [Article Influence: 7.7] [Reference Citation Analysis]
22 Shafi S, Khan S, Hoda F, Fayaz F, Singh A, Khan MA, Ali R, Pottoo FH, Tariq S, Najmi AK. Decoding Novel Mechanisms and Emerging Therapeutic Strategies in Breast Cancer Resistance. CDM 2020;21:199-210. [DOI: 10.2174/1389200221666200303124946] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
23 Pan L, Ren P, Xu Z. Therapeutic Schedule Evaluation for Brain-Metastasized Non-Small Cell Lung Cancer with A Probabilistic Linguistic ELECTRE II Method. Int J Environ Res Public Health 2018;15:E1799. [PMID: 30134591 DOI: 10.3390/ijerph15091799] [Cited by in Crossref: 11] [Cited by in F6Publishing: 1] [Article Influence: 2.8] [Reference Citation Analysis]
24 Nedungadi P, Iyer A, Gutjahr G, Bhaskar J, Pillai AB. Data-Driven Methods for Advancing Precision Oncology. Curr Pharmacol Rep 2018;4:145-56. [PMID: 33520605 DOI: 10.1007/s40495-018-0127-4] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
25 Sun X, Liu X, Xia M, Shao Y, Zhang XD. Multicellular gene network analysis identifies a macrophage-related gene signature predictive of therapeutic response and prognosis of gliomas. J Transl Med 2019;17:159. [PMID: 31097021 DOI: 10.1186/s12967-019-1908-1] [Cited by in Crossref: 12] [Cited by in F6Publishing: 13] [Article Influence: 4.0] [Reference Citation Analysis]
26 West H, Roberts F, Sweeney P, Walker-Samuel S, Leedale J, Colley H, Murdoch C, Shipley RJ, Webb S. A mathematical investigation into the uptake kinetics of nanoparticles in vitro. PLoS One 2021;16:e0254208. [PMID: 34292999 DOI: 10.1371/journal.pone.0254208] [Reference Citation Analysis]
27 Alves R, Gonçalves AC, Rutella S, Almeida AM, De Las Rivas J, Trougakos IP, Sarmento Ribeiro AB. Resistance to Tyrosine Kinase Inhibitors in Chronic Myeloid Leukemia-From Molecular Mechanisms to Clinical Relevance. Cancers (Basel) 2021;13:4820. [PMID: 34638304 DOI: 10.3390/cancers13194820] [Reference Citation Analysis]
28 Lai X, Hao W, Friedman A. TNF-α inhibitor reduces drug-resistance to anti-PD-1: A mathematical model. PLoS One 2020;15:e0231499. [PMID: 32310956 DOI: 10.1371/journal.pone.0231499] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
29 Song Q, Hawkins GA, Wudel L, Chou PC, Forbes E, Pullikuth AK, Liu L, Jin G, Craddock L, Topaloglu U, Kucera G, O'Neill S, Levine EA, Sun P, Watabe K, Lu Y, Alexander-Miller MA, Pasche B, Miller LD, Zhang W. Dissecting intratumoral myeloid cell plasticity by single cell RNA-seq. Cancer Med 2019;8:3072-85. [PMID: 31033233 DOI: 10.1002/cam4.2113] [Cited by in Crossref: 39] [Cited by in F6Publishing: 34] [Article Influence: 13.0] [Reference Citation Analysis]
30 Park Y, Heider D, Hauschild AC. Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence. Cancers (Basel) 2021;13:3148. [PMID: 34202427 DOI: 10.3390/cancers13133148] [Reference Citation Analysis]
31 Parri M, Ippolito L, Cirri P, Ramazzotti M, Chiarugi P. Metabolic cell communication within tumour microenvironment: models, methods and perspectives. Curr Opin Biotechnol 2020;63:210-9. [PMID: 32416546 DOI: 10.1016/j.copbio.2020.03.001] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]