Stochastic-Processes
My book: Gentle Introduction to Chaotic Dynamical Systems. Includes stochastic dynamical systems and statistical properties of numeration systems in any dimension. (by VincentGranville)
dynamo-release
Inclusive model of expression dynamics with conventional or metabolic labeling based scRNA-seq / multiomics, vector field reconstruction and differential geometry analyses (by aristoteleo)
Stochastic-Processes | dynamo-release | |
---|---|---|
1 | 2 | |
30 | 405 | |
- | 2.5% | |
6.4 | 9.6 | |
about 1 year ago | 7 days ago | |
Python | Python | |
- | BSD 3-clause "New" or "Revised" License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stochastic-Processes
Posts with mentions or reviews of Stochastic-Processes.
We have used some of these posts to build our list of alternatives
and similar projects.
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New Book: Gentle Introduction To Chaotic Dynamical Systems
Authored by Dr. Vincent Granville, 82 pages, published in March 2023. Available on our e-Store exclusively, here. See the table contents or sample chapter on GitHub here. The Python code is also in the same repository.
dynamo-release
Posts with mentions or reviews of dynamo-release.
We have used some of these posts to build our list of alternatives
and similar projects.
-
MIT Researchers Open-Source ‘Dynamo’: A Machine Learning-Based Python Framework For Gaining Insights Into Dynamic Biological Processes
The framework is named “dynamo” and can also determine the underlying mechanisms that drive cell changes. Their research focused on how cells change over time rather than how they migrate through space. Continue Reading
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Mapping transcriptomic vector fields of single cells (Feb 2022)
Single-cell (sc)RNA-seq, together with RNA velocity and metabolic labeling, reveals cellular states and transitions at unprecedented resolution. Fully exploiting these data, however, requires kinetic models capable of unveiling governing regulatory functions. Here, we introduce an analytical framework dynamo (https://github.com/aristoteleo/dynamo-release), which infers absolute RNA velocity, reconstructs continuous vector fields that predict cell fates, employs differential geometry to extract underlying regulations, and ultimately predicts optimal reprogramming paths and perturbation outcomes. We highlight dynamo’s power to overcome fundamental limitations of conventional splicing-based RNA velocity analyses to enable accurate velocity estimations on a metabolically labeled human hematopoiesis scRNA-seq dataset. Furthermore, differential geometry analyses reveal mechanisms driving early megakaryocyte appearance and elucidate asymmetrical regulation within the PU.1-GATA1 circuit. Leveraging the least-action-path method, dynamo accurately predicts drivers of numerous hematopoietic transitions. Finally, in silico perturbations predict cell-fate diversions induced by gene perturbations. Dynamo, thus, represents an important step in advancing quantitative and predictive theories of cell-state transitions.
What are some alternatives?
When comparing Stochastic-Processes and dynamo-release you can also consider the following projects:
diffrax - Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
pysindy - A package for the sparse identification of nonlinear dynamical systems from data
GPflow - Gaussian processes in TensorFlow
NeuralCDE - Code for "Neural Controlled Differential Equations for Irregular Time Series" (Neurips 2020 Spotlight)
torchsde - Differentiable SDE solvers with GPU support and efficient sensitivity analysis.
Point-Processes - This repository contains the material (datasets, code, videos, spreadsheets) related to my book Stochastic Processes and Simulations - A Machine Learning Perspective.