Mary E. Sehl, Ph.D.
Institution:
University of California, Los Angeles, Los Angeles, CAGoogle:
"Mary Sehl"Mean distance: 18.33
Parents
Sign in to add mentor| Kenneth Lamar Lange | grad student | 2009 | UCLA | |
| (Stochastic dynamics of cancer stem cells.) | ||||
| Janet S. Sinsheimer | grad student | 2009 | UCLA (MathTree) | |
| (Stochastic dynamics of cancer stem cells.) | ||||
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Publications
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| Stutz TC, Sinsheimer JS, Sehl M, et al. (2021) Computational tools for assessing gene therapy under branching process models of mutation. Bulletin of Mathematical Biology. 84: 15 |
| Landeros A, Ji X, Lange K, et al. (2021) An examination of school reopening strategies during the SARS-CoV-2 pandemic. Plos One. 16: e0251242 |
| Stutz TC, Landeros A, Xu J, et al. (2021) Stochastic simulation algorithms for Interacting Particle Systems. Plos One. 16: e0247046 |
| Landeros A, Ji X, Lange KL, et al. (2020) An Examination of School Reopening Strategies during the SARS-CoV-2 Pandemic. Medrxiv : the Preprint Server For Health Sciences |
| Sehl M, Henry J, Storniolo A, et al. (2019) Abstract P1-09-08: Hormonal factors associated with elevation of DNA methylation age in breast tissue of healthy women Cancer Research. 79 |
| Bolanos R, Martinez-Maza O, Zhang ZF, et al. (2018) Decreased levels of the serum inflammatory biomarkers, sGP130, IL-6, sCRP and BAFF, are associated with increased likelihood of AIDS related Kaposi's sarcoma in men who have sex with men. Cancer Research Frontiers. 4: 45-59 |
| Landeros A, Stutz T, Keys KL, et al. (2018) BioSimulator.jl: Stochastic simulation in Julia. Computer Methods and Programs in Biomedicine. 167: 23-35 |
| Sehl ME, Wicha MS. (2018) Modeling of Interactions between Cancer Stem Cells and their Microenvironment: Predicting Clinical Response. Methods in Molecular Biology (Clifton, N.J.). 1711: 333-349 |
| Sehl ME, Ganz PA, Horvath S. (2016) Abstract 4444: DNA methylation age is accelerated in breast tissue of healthy women donors Cancer Research. 76: 4444-4444 |
| Sehl ME, Shimada M, Landeros A, et al. (2015) Modeling of Cancer Stem Cell State Transitions Predicts Therapeutic Response. Plos One. 10: e0135797 |