pna
graphein
pna | graphein | |
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5 | 2 | |
329 | 988 | |
- | - | |
1.8 | 7.8 | |
almost 2 years ago | 14 days ago | |
Python | Jupyter Notebook | |
MIT License | MIT License |
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pna
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[D] Machine Learning - WAYR (What Are You Reading) - Week 130
/u/CatalyzeX_code_bot: Paper link
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[D] Machine Learning - WAYR (What Are You Reading) - Week 129
Code for https://arxiv.org/abs/2004.05718 found: https://github.com/lukecavabarrett/pna
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[D] Machine Learning - WAYR (What Are You Reading) - Week 128
In this regard, I came across the Principal Neighborhood Aggregation paper which aggregates information from neighbors based on different aggregators (1st, 2nd, and higher-order moments like mean, std, kurtosis, etc.). Additionally, the authors also introduce a scaler based on the degree of the node. It basically amplifies or attenuates the incoming information from the neighboring nodes. The code implementation is available here. Reach out to me if you want to discuss more!
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.
What are some alternatives?
gnn-lspe - Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022
pytorch_geometric_temporal - PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
torchdrug - A powerful and flexible machine learning platform for drug discovery
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
geometric-gnn-dojo - Geometric GNN Dojo provides unified implementations and experiments to explore the design space of Geometric Graph Neural Networks.
benchmarking-gnns - Repository for benchmarking graph neural networks