Contextualizing protein representations using deep learning on protein networks and single-cell data
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Updated
May 23, 2024 - Python
Contextualizing protein representations using deep learning on protein networks and single-cell data
Package for the data-driven representation of non-linear dynamics over manifolds based on a statistical distribution of local phase portrait features. Includes specific example on dynamical systems, synthetic- and real neural datasets. https://agosztolai.github.io/MARBLE/
Graph Neural Network Library for PyTorch
Redes convolucionales definidas en grafos para la predicción de nuevas asociaciones gen-enfermedad
gRNAde: Geometric Deep Learning for 3D RNA inverse design
Protein Graph Library
设计一下怎么毕业
A library for differentiable robotics.
Target-aware Variational Auto-encoders for Ligand Generation with Multimodal Protein Representation Learning
A novel architecture and training strategy for graph neural networks (GNN). The proposed architecture, named as Autoencoder-Aided GNN (AA-GNN), compresses the convolutional features at multiple hidden layers, hinging on a novel end-to-end training procedure that learns different graph representations per each layer. As a result, the computationa…
Continuous regular group convolutions for Pytorch
Low-Level Graph Neural Network Operators for PyG
Python Framework built on PyTorch and PyTorch Geometric for working with Representation Learning on Graph Neural Networks.
PyNeuraLogic lets you use Python to create Differentiable Logic Programs
Triangle mesh deep learning utility library.
Pytorch Implementation of Group-equivariant Convolutional Networks
A scalable graph learning toolkit for extremely large graph datasets. (WWW'22, 🏆 Best Student Paper Award)
[ICLR'24] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
Implementation of PocketGen: Generating Full-Atom Ligand-Binding Protein Pockets
A curated list of topological deep learning (TDL) resources and links.
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