JiahaoChow

scMultiBench

Multi-task benchmarking of single-cell multimodal omics integration methods

midas

StereoMMv1

StereoMM is a graph fusion model that can integrate gene expression, histological images, and spatial location. And the information interaction within modalities is strengthened by introducing an attention mechanism.

HGGEP

Gene Expression Prediction from Histology Images via Hypergraph Neural Networks

TANGLE

Transcriptomics-guided Slide Representation Learning in Computational Pathology - CVPR 2024

BLEEP

Spatially Resolved Gene Expression Prediction from H&E Histology Images via Bi-modal Contrastive Learning

conv_meta

Code of《Improved Transfer-Meta Framework Base on Attention Embedding for Cross-domain Recommendation to Cold-Start Users》

awesome-self-supervised-learning-for-graphs

A curated list for awesome self-supervised learning for graphs.

liger

R package for integrating and analyzing multiple single-cell datasets

dance

DANCE: a deep learning library and benchmark platform for single-cell analysis

scBERT

scarches

Reference mapping for single-cell genomics

Concerto-reproducibility

symphony

Efficient and precise single-cell reference atlas mapping with Symphony

SCALEX

Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space

fgc

variational-continual-learning

Implementation of the variational continual learning method

Gradient-based-Sample-Selection

core50

CORe50: a new Dataset and Benchmark for Continual Learning

Adversarial-Continual-Learning

Implementation for the paper "Adversarial Continual Learning" in PyTorch.

Continual-Learning-Benchmark

Evaluate three types of task shifting with popular continual learning algorithms.

LearningWithoutForgetting

Repository for the Learning without Forgetting paper, ECCV 2016

brain-inspired-replay

A brain-inspired version of generative replay for continual learning with deep neural networks (e.g., class-incremental learning on CIFAR-100; PyTorch code).

continual-learning

PyTorch implementation of various methods for continual learning (XdG, EWC, SI, LwF, FROMP, DGR, BI-R, ER, A-GEM, iCaRL, Generative Classifier) in three different scenarios.

PathomicFusion

Fusing Histology and Genomics via Deep Learning - IEEE TMI

MLA-GNN

graph_nets

PyTorch Implementation and Explanation of Graph Representation Learning papers: DeepWalk, GCN, GraphSAGE, ChebNet & GAT.

JointSVD

Code for the paper "Joint Matrix Decomposition for Deep Convolutional Neural Networks Compression"