| Nonlinear modelling of cancer: bridging the gap between cells and tumours JS Lowengrub, HB Frieboes, F Jin, YL Chuang, X Li, P Macklin, SM Wise, ... Nonlinearity 23 (1), R1, 2009 | 663 | 2009 |
| Multiscale Cancer Modeling TS Deisboeck, Z Wang, P Macklin, V Cristini Annual Review of Biomedical Engineering 13, 127-155, 2011 | 563 | 2011 |
| Multiscale modelling and nonlinear simulation of vascular tumour growth P Macklin, S McDougall, ARA Anderson, MAJ Chaplain, V Cristini, ... Journal of mathematical biology 58 (4), 765-798, 2009 | 443 | 2009 |
| The human body at cellular resolution: the NIH Human Biomolecular Atlas Program Caltech-UW TMC Cai Long lcai@ caltech. edu 21 b Shendure Jay 9 Trapnell Cole ... Nature 574 (7777), 187-192, 2019 | 400 | 2019 |
| PhysiCell: an Open Source Physics-Based Cell Simulator for 3-D Multicellular Systems A Ghaffarizadeh, R Heiland, SH Friedman, SM Mumenthaler, P Macklin PLoS Computational Biology 14 (2), e1005991, 2018 | 381 | 2018 |
| A review of cell-based computational modeling in cancer biology J Metzcar, Y Wang, R Heiland, P Macklin JCO clinical cancer informatics 2, 1-13, 2019 | 358 | 2019 |
| Computer simulation of glioma growth and morphology HB Frieboes, JS Lowengrub, S Wise, X Zheng, P Macklin, EL Bearer, ... Neuroimage 37, S59-S70, 2007 | 277 | 2007 |
| Patient-calibrated agent-based modelling of ductal carcinoma in situ (DCIS): From microscopic measurements to macroscopic predictions of clinical progression P Macklin, ME Edgerton, AM Thompson, V Cristini J. Theor. Biol. 301, 122-140, 2012 | 266 | 2012 |
| Nonlinear simulation of the effect of microenvironment on tumor growth P Macklin, J Lowengrub Journal of theoretical biology 245 (4), 677-704, 2007 | 244 | 2007 |
| The cancer microbiome: distinguishing direct and indirect effects requires a systemic view JB Xavier, VB Young, J Skufca, F Ginty, T Testerman, AT Pearson, ... Trends in cancer 6 (3), 192-204, 2020 | 192 | 2020 |
| The 2019 mathematical oncology roadmap RC Rockne, A Hawkins-Daarud, KR Swanson, JP Sluka, JA Glazier, ... Physical biology 16 (4), 041005, 2019 | 177 | 2019 |
| Evolving interfaces via gradients of geometry-dependent interior Poisson problems: application to tumor growth P Macklin, J Lowengrub Journal of Computational Physics 203 (1), 191-220, 2005 | 128 | 2005 |
| PhysiBoSS: a multi-scale agent-based modelling framework integrating physical dimension and cell signalling G Letort, A Montagud, G Stoll, R Heiland, E Barillot, P Macklin, A Zinovyev, ... Bioinformatics 35 (7), 1188-1196, 2019 | 113 | 2019 |
| A new ghost cell/level set method for moving boundary problems: application to tumor growth P Macklin, JS Lowengrub Journal of scientific computing 35, 266-299, 2008 | 113 | 2008 |
| An improved geometry-aware curvature discretization for level set methods: application to tumor growth P Macklin, J Lowengrub journal of Computational Physics 215 (2), 392-401, 2006 | 106 | 2006 |
| Digital twins for predictive oncology will be a paradigm shift for precision cancer care T Hernandez-Boussard, P Macklin, EJ Greenspan, AL Gryshuk, ... Nature medicine 27 (12), 2065-2066, 2021 | 105 | 2021 |
| BioFVM: an efficient, parallelized diffusive transport solver for 3-D biological simulations A Ghaffarizadeh, SH Friedman, P Macklin Bioinformatics, 2015 | 103 | 2015 |
| Building digital twins of the human immune system: toward a roadmap R Laubenbacher, A Niarakis, T Helikar, G An, B Shapiro, RS Malik-Sheriff, ... NPJ digital medicine 5 (1), 64, 2022 | 70 | 2022 |
| High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow J Ozik, N Collier, JM Wozniak, C Macal, C Cockrell, SH Friedman, ... BMC bioinformatics 19, 81-97, 2018 | 69 | 2018 |
| Agent-based modeling of cancer stem cell driven solid tumor growth J Poleszczuk, P Macklin, H Enderling Stem cell heterogeneity: Methods and protocols, 335-346, 2016 | 61 | 2016 |
Vittorio Cristini, PhD, ISI Highly-Cited -...Professor of Mathematical Medicine, Houston Methodist RI在 houstonmethodist.org 的电子邮件经过验证
