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awesome-efficient-gnn

Efficient Graph Neural Networks - a curated list of papers and projects

benchmarking-gnns

Repository for benchmarking graph neural networks

bert

TensorFlow code and pre-trained models for BERT

bidd-molmap

MolMapNet: An Efficient Convolutional Neural Network Based on Constructed Feature Maps for Molecular Deep Learning

capsnet-pytorch

PyTorch implementation of NIPS 2017 paper Dynamic Routing Between Capsules

capsnet_ptm

CapsNet for Protein Post-translational Modification site prediction.

clam

Data-efficient and weakly supervised computational pathology on whole slide images - Nature Biomedical Engineering

clear

CLEAR: Self-supervised contrastive learning for integrative single-cell RNA-seq data analysis

conformer

d2l-zh

《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被全球175所大学采用教学。

deep-learning-with-tensorflow-book

深度学习入门开源书，基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework.

deeppath

Classification of Lung cancer slide images using deep-learning

dgl

Python package built to ease deep learning on graph, on top of existing DL frameworks.

dm3loc

A novel Deep-learning Framework with Multi-head Self-attention for Multi-label mRNA Subcellular Localization Prediction and Analyses

dmpfold

De novo protein structure prediction using iteratively predicted structural constraints

emogi

An explainable multi-omics graph integration method based on graph convolutional networks to predict cancer genes.

gan

python notebooks accompanying the book Make Your Own GAN

gemma

Genome-wide Efficient Mixed Model Association

graphembedding

Implementation and experiments of graph embedding algorithms.

graphneuralnetwork

《深入浅出图神经网络：GNN原理解析》配套代码

graphsage-pytorch

A PyTorch implementation of GraphSAGE. This package contains a PyTorch implementation of GraphSAGE.

ideepc

imagemol

moco

PyTorch implementation of MoCo: https://arxiv.org/abs/1911.05722

moldiscovery

molecular-graph-bert

semi-supervised learning for molecular property prediction

molrep

MolRep: A Deep Representation Learning Library for Molecular Property Prediction

musitedeep

MusiteDeep provides a deep-learning method for general and kinase-specific phosphorylation site prediction. It is implemented by deep learning library Keras and Theano backend (the Keras2.0 and Tensorflow backend implementation were also provided under folder MusiteDeep_Keras2.0). At present, MusiteDeep only provides prediction of human phosphorylation sites; however, it also provides customized model training that enables users to train other PTM prediction models by using their own training data sets based on either CPU or GPU.

npeet

Non-parametric Entropy Estimation Toolbox

nucleicnet