150 Open source projects that are alternatives of or similar to ProteinGCN

A distributed graph deep learning framework.

links to conference publications in graph-based deep learning

A PyTorch implementation of "SelfGNN: Self-supervised Graph Neural Networks without explicit negative sampling" paper, which appeared in The International Workshop on Self-Supervised Learning for the Web (SSL'21) @ the Web Conference 2021 (WWW'21).

StellarGraph - Machine Learning on Graphs

Summary of open source code for deep learning models in the field of traffic prediction

Code and resources on scalable and efficient Graph Neural Networks

Representation Learning on Graphs with Jumping Knowledge Networks

Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning (authors' MXNet implementation for the TACL19 paper)

A reading list for deep graph learning acceleration.

Graph Neural Network Library for PyTorch

Tutorials for Machine Learning on Graphs

Implementation of the WSDM 2021 paper "Node Similarity Preserving Graph Convolutional Networks"

An index of recommendation algorithms that are based on Graph Neural Networks.

Codes for "Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks" paper

Representing Long-Range Context for Graph Neural Networks with Global Attention

Papers about explainability of GNNs

Guided Conditional Wasserstein GAN for De Novo Protein Design

Neural Networks for Protein Sequence Alignment

GraphNVP: An Invertible Flow Model for Generating Molecular Graphs

Protein structure comparison tools such as SSAP and SNAP

Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits

PyTorch implementation of our paper "Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing" (NAACL19)

Modelling the Language of Life - Deep Learning Protein Sequences

Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022

Probabilistic Type Inference using Graph Neural Networks

Implementation of paper "Self-supervised Learning on Graphs:Deep Insights and New Directions"

An End-to-End Machine Learning Library to Optimize AUC (AUROC, AUPRC).

ACL 2018: Dating Documents using Graph Convolution Networks

Code for the BMVC paper (http://bmvc2018.org/contents/papers/1003.pdf)

ProtVec can be used in protein interaction predictions, structure prediction, and protein data visualization.

A graph-based deep learning framework for life science

Protein-protein, protein-peptide and protein-DNA docking framework based on the GSO algorithm

From Random Walks to Transformer for Learning Node Embeddings (ECML-PKDD 2020) (In Pytorch and Tensorflow)

[ACL 2021] KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction

This is an open-source toolkit for Heterogeneous Graph Neural Network(OpenHGNN) based on DGL.

Geometric Deep Learning for Flux

GemNet model in PyTorch, as proposed in "GemNet: Universal Directional Graph Neural Networks for Molecules" (NeurIPS 2021)

Resources for the paper titled "EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network". Accepted for publication (with an oral spotlight!) at ML4H Workshop, NeurIPS 2020.

This is a paper list about Resource Allocation in Network Functions Virtualization (NFV) and Software-Defined Networking (SDN).

Pytorch/Python3 implementation of DeepAccNet, protein model accuracy evaluator.

[IJCAI 2019] Source code and datasets for "Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification"

Official implementation of Neural Bellman-Ford Networks (NeurIPS 2021)

Code for the paper 'An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem' (INFORMS Annual Meeting Session 2019)

Graph-based machine learning for chemical property prediction

A Context-aware Visual Attention-based training pipeline for Object Detection from a Webpage screenshot!

Smart Contract Vulnerability Detection Using Graph Neural Networks (IJCAI-20 Accepted)

Quaternion Graph Neural Networks (ACML 2021) (Pytorch and Tensorflow)

The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL), NeurIPS-2021

Bioinformatics toolkits for manipulating sequence, alignment, and phylogenetic tree files

TypeDB-ML is the Machine Learning integrations library for TypeDB

Message Passing Neural Networks for Simplicial and Cell Complexes

Supervised community detection with line graph neural networks

A curated list of awesome graph & self-supervised-learning-based recommendation.

⚡ Using deep learning (MLP, CNN, Graph CNN) to classify text in TensorFlow.

Tensorflow Implementation of the paper "Topology Adaptive Graph Convolutional Networks" (Du et al., 2017)

Code & data accompanying the ICLR 2020 paper "Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation"

TIMME: Twitter Ideology-detection via Multi-task Multi-relational Embedding (code & data)

A list of data mining and machine learning papers that I implemented in 2019.

Locally Private Graph Neural Networks (ACM CCS 2021)

FluentDNA allows you to browse sequence data of any size using a zooming visualization similar to Google Maps. You can use FluentDNA as a standalone program or as a python module for your own bioinformatics projects.