Other Open Access
These notebooks demonstrate Most Permissive Boolean Networks (doi:10.1101/2020.03.22.998377) on several case studies of biological networks.
They can be executed interactively within the CoLoMoTo Docker version 2020-07-01
:
notebooks
is the folder in which you extracted the notebooks:
sudo pip install -U colomoto-docker # you may have to use pip3 instead of pip
colomoto-docker -V 2020-07-01 --bind notebooks
Visualize online:
The notebooks rely on the Python library mpbn, see the documentation for usage and examples at https://mpbn.readthedocs.io
Name | Size | |
---|---|---|
bonesis-preview-20200701.zip
md5:958161ca649ac91aad84b4dc89535dc7 |
32.2 kB | Download |
Dockerfile
md5:e8b15575e2de6864d270debc5a408bf5 |
198 Bytes | Download |
I3FFL - compatible MPBNs.ipynb
md5:adfa465fb4fec42ff75070e80fbdb5ad |
10.4 kB | Download |
MPBN applied to Bladder Tumorigenesis by Remy et al 2015.ipynb
md5:e3d7e324f7cf193bf79402c7c70716a4 |
116.2 kB | Download |
MPBN applied to T-Cell differentiation model by Abou-Jaoudé et al. 2015.ipynb
md5:24f93796a30972d8711200cdaa0cd3bf |
191.6 kB | Download |
MPBN applied to Tumour invasion model by Cohen et al. 2015.ipynb
md5:2d4c1f697ff8ee844b4acfdb52ffc66c |
70.9 kB | Download |
Scalability on large random BNs.ipynb
md5:64b4aaa5b22b12933b30ca5e43f0e248 |
50.3 kB | Download |
All versions | This version | |
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