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Found 7 papers, 0 papers with code
Modeling the hallmarks of avascular tumors
We present a stochastic computational model of avascular tumors, emphasizing the detailed implementation of the first four so-called hallmarks of cancer: self-sufficiency in growth factors, resistance to growth inhibitors, avoidance of apoptosis, and unlimited growth potential.
Robust and integrative Bayesian neural networks for likelihood-free parameter inference
State-of-the-art neural network-based methods for learning summary statistics have delivered promising results for simulation-based likelihood-free parameter inference.
Flash X-ray diffraction imaging in 3D: a proposed analysis pipeline
Modern Flash X-ray diffraction Imaging (FXI) acquires diffraction signals from single biomolecules at a high repetition rate from X-ray Free Electron Lasers (XFELs), easily obtaining millions of 2D diffraction patterns from a single experiment.
Supervised Classification Methods for Flash X-ray single particle diffraction Imaging
It is also straightforward to parallelize them so as to fully match the XFEL repetition rate, thereby enabling processing at site.
Assessing Uncertainties in X-ray Single-particle Three-dimensional reconstructions
These two-dimensional diffraction patterns can be practically reconstructed and retrieved down to a resolution of a few \angstrom.
SimInf: An R package for Data-driven Stochastic Disease Spread Simulations
We present the R package SimInf which provides an efficient and very flexible framework to conduct data-driven epidemiological modeling in realistic large scale disease spread simulations.
Machine learning for ultrafast X-ray diffraction patterns on large-scale GPU clusters
With the expected enormous amount of diffraction data to be produced in the foreseeable future, this is the required scale to approach real time processing of data at the beam site.
