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pro vyhledávání: '"Hlavacek, William S"'
Autor:
Neumann, Jacob, Lin, Yen Ting, Mallela, Abhishek, Miller, Ely F., Colvin, Joshua, Duprat1, Abell T., Chen, Ye, Hlavacek, William S., Posner, Richard G.
Bayesian inference in biological modeling commonly relies on Markov chain Monte Carlo (MCMC) sampling of a multidimensional and non-Gaussian posterior distribution that is not analytically tractable. Here, we present the implementation of a practical
Externí odkaz:
http://arxiv.org/abs/2109.14445
Publikováno v:
In Epidemics December 2023 45
Autor:
Lin, Yen Ting, Neumann, Jacob, Miller, Ely, Posner, Richard G., Mallela, Abhishek, Safta, Cosmin, Ray, Jaideep, Thakur, Gautam, Chinthavali, Supriya, Hlavacek, William S.
To increase situational awareness and support evidence-based policy-making, we formulated two types of mathematical models for COVID-19 transmission within a regional population. One is a fitting function that can be calibrated to reproduce an epidem
Externí odkaz:
http://arxiv.org/abs/2007.12523
Autor:
Mitra, Eshan D., Hlavacek, William S.
Motivation: Recent work has demonstrated the feasibility of using non-numerical, qualitative data to parameterize mathematical models. However, uncertainty quantification (UQ) of such parameterized models has remained challenging because of a lack of
Externí odkaz:
http://arxiv.org/abs/1909.00072
Autor:
Mitra, Eshan D., Hlavacek, William S.
Mathematical models can provide quantitative insight into immunoreceptor signaling, but require parameterization and uncertainty quantification before making reliable predictions. We review currently available methods and software tools to address th
Externí odkaz:
http://arxiv.org/abs/1906.11365
Various kinetic Monte Carlo algorithms become inefficient when some of the population sizes in a system are large, which gives rise to a large number of reaction events per unit time. Here, we present a new acceleration algorithm based on adaptive an
Externí odkaz:
http://arxiv.org/abs/1903.08615
Autor:
Mitra, Eshan D., Suderman, Ryan, Colvin, Joshua, Ionkov, Alexander, Hu, Andrew, Sauro, Herbert M., Posner, Richard G., Hlavacek, William S.
In systems biology modeling, important steps include model parameterization, uncertainty quantification, and evaluation of agreement with experimental observations. To help modelers perform these steps, we developed the software PyBioNetFit. PyBioNet
Externí odkaz:
http://arxiv.org/abs/1903.07750
Autor:
Hlavacek, William S., Longo, Jennifer, Baker, Lewis R., Álamo, María del Carmen Ramos, Ionkov, Alexander, Mitra, Eshan D., Suderman, Ryan, Erickson, Keesha E., Dias, Raquel, Colvin, Joshua, Thomas, Brandon R., Posner, Richard G.
BioNetFit is a software tool designed for solving parameter identification problems that arise in the development of rule-based models. It solves these problems through curve fitting (i.e., nonlinear regression). BioNetFit is compatible with determin
Externí odkaz:
http://arxiv.org/abs/1809.08321
Akademický článek
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Autor:
Shirin, Afroza, Klickstein, Isaac, Feng, Song, Lin, Yen Ting, Hlavacek, William S., Sorrentino, Francesco
The effects of molecularly targeted drug perturbations on cellular activities and fates are difficult to predict using intuition alone because of the complex behaviors of cellular regulatory networks. An approach to overcoming this problem is to deve
Externí odkaz:
http://arxiv.org/abs/1808.00545