genome_integration
pgmpy
genome_integration | pgmpy | |
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1 | 2 | |
11 | 2,624 | |
- | 0.8% | |
0.0 | 8.0 | |
almost 2 years ago | 10 days ago | |
Python | Python | |
MIT License | MIT License |
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genome_integration
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[D] Clustering high dimensional
Causal Genomic Analysis
pgmpy
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Use data from tables generated in python console,
No need to post the help, here is the DiscreteFactor class https://github.com/pgmpy/pgmpy/blob/eb65f40d2b32bf2ad971181333bb9ed7aefde907/pgmpy/factors/discrete/DiscreteFactor.py
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[D] Python toolboxes for probabilistic graphical model inference
I do know of a few promising toolboxes such as pgmpy, pymc3, and pyro, but have not used either of them (for this purpose) and am at a bit of a loss picking one to start with.
What are some alternatives?
causalnex - A Python library that helps data scientists to infer causation rather than observing correlation.
CausalPy - A Python package for causal inference in quasi-experimental settings
statsmodels - Statsmodels: statistical modeling and econometrics in Python
enformer-pytorch - Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch
scikit-learn - scikit-learn: machine learning in Python
awesome-causality-algorithms - An index of algorithms for learning causality with data
causalml - Uplift modeling and causal inference with machine learning algorithms
rustworkx - A high performance Python graph library implemented in Rust.
dowhy - DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
pyhf - pure-Python HistFactory implementation with tensors and autodiff