longyahui Goto Github PK

annoy

Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk

awesome-foundation-model-single-cell-papers

awesome-multi-omics

List of software packages for multi-omics analysis

cell2location

Comprehensive mapping of tissue cell architecture via integrated single cell and spatial transcriptomics (cell2location model)

celseq2

Generate the UMI count matrix from CEL-Seq2 sequencing data

cg3

This is the repository for the AAAI 21 paper [Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning].

cloob

dance

DANCE: A Deep Learning Library for Single-Cell Analysis

dti-datasets

entity_alignment_papers

Must-read papers on entity alignment published in recent years

gcnmda

graph-based-deep-learning-literature

links to conference publications in graph-based deep learning

graph-neural-network-papers

Graph Neural Network Paper List

graph-prediction-with-fused-gromov-wasserstein

Python implementation of the supervised graph prediction method proposed in http://arxiv.org/abs/2202.03813 using PyTorch library and POT library (Python Optimal Transport).

home

learnpython

Some codes in the process of learning Python language.

mcgc

pot

POT : Python Optimal Transport

pt-gnn

pygat

Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903)

pygcn

Graph Convolutional Networks in PyTorch

rna-seq-and-atac-seq-mapping

scanpy

Single-cell analysis in Python. Scales to >1M cells.

spagcn

SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network

sparx

Elucidate spatial heterogeneity of drug sensitivity for personalized treatment

spatialbenchmarking

spatialglue_tutorial

sprod

SPROD: Spatial expression profiling de-noising

stagate

Adaptive Graph Attention Auto-encoder for Spatial Domain Identification of Spatial Transcriptomics

stagate_tutorials