graphein
geometric-gnn-dojo
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| graphein | geometric-gnn-dojo | |
|---|---|---|
| 2 | 4 | |
| 978 | 415 | |
| - | - | |
| 7.8 | 3.8 | |
| 6 days ago | 11 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | MIT License |
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graphein
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Meet Graphein: a Python Library for Geometric Deep Learning and Network Analysis on Protein Structures and Interaction Networks
Github: https://github.com/a-r-j/graphein
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[Discussion] which NN architecture is best suitable for analysing the structural data of biomolecules
As alluded to by u/WorldWar1Nerd, it depends on the format and structure of your data. However, based on what you have already said about your dataset, a graph neural network (GNN) may be a suitable choice, depending on the task. I recommend looking into a wonderful ML library for proteins called Graphein (https://github.com/a-r-j/graphein) to get started, however, do not be afraid if you find that you need to customize these methods to your specific problem.
geometric-gnn-dojo
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Gentle Introduction to Geometric Graph Neural Networks
Geometric GNNs are an emerging class of GNNs for spatially embedded graphs in scientific and engineering applications, s.a. biomolecular structure, material science, and physical simulations. Notable examples include SchNet, DimeNet, Tensor Field Networks, and E(n) Equivariant GNNs.
https://github.com/chaitjo/geometric-gnn-dojo/blob/main/geom...
This notebook and repository aims to serve as a 'Geometric GNNs 101' introduction for newcomers.
We walk through the basics of GNNs, Geometric Deep Learning, and the PyTorch Geometric library for implementing these concepts.
Our goal is to help students understand how theory/equations connect to real code.
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[R] On the Expressive Power of Geometric Graph Neural Networks
Found relevant code at https://github.com/chaitjo/geometric-gnn-dojo + all code implementations here
What are some alternatives?
pytorch_geometric_temporal - PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
AllenSDK - code for reading and processing Allen Institute for Brain Science data
fastai - The fastai deep learning library
netsci-labs - (In progress) Network science laboratories. Covers graph theory, random graphs and ML on graphs
ktrain - ktrain is a Python library that makes deep learning and AI more accessible and easier to apply
benchmarking-gnns - Repository for benchmarking graph neural networks