C0nc

BlogBackup

博客备份

BSP

c0nc.github.io

CASP

code for "Discovering Causal Relationship in Spatial Proteomics Data with CASP"

CompGCN

ICLR 2020: Composition-Based Multi-Relational Graph Convolutional Networks

CppTemplateTutorial

中文的C++ Template的教学指南。与知名书籍C++ Templates不同，该系列教程将C++ Templates作为一门图灵完备的语言来讲授，以求帮助读者对Meta-Programming融会贯通。(正在施工中)

EMOGI

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

River

A Python package for identification Differential Spatial Expression Pattern (DESP) gene by interpretable deep learning from multi-slice spatial omics data.

SMG

SMG: self-supervised masked graph learning for cancer gene identification.

TAICHI

A Python package for the Scalable and accurate identification condition-relevant niches from spatial -omics data.

C0nc/River

A Python package for identification Differential Spatial Expression Pattern (DESP) gene by interpretable deep learning from multi-slice spatial omics data.

C0nc/SMG

SMG: self-supervised masked graph learning for cancer gene identification.

C0nc/TAICHI

A Python package for the Scalable and accurate identification condition-relevant niches from spatial -omics data.

C0nc/BlogBackup

博客备份

C0nc/BSP

C0nc/c0nc.github.io

C0nc/CASP

code for "Discovering Causal Relationship in Spatial Proteomics Data with CASP"

C0nc/CompGCN

ICLR 2020: Composition-Based Multi-Relational Graph Convolutional Networks

C0nc/CppTemplateTutorial

中文的C++ Template的教学指南。与知名书籍C++ Templates不同，该系列教程将C++ Templates作为一门图灵完备的语言来讲授，以求帮助读者对Meta-Programming融会贯通。(正在施工中)

C0nc/EMOGI

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

C0nc/FS-Mol

FS-Mol is A Few-Shot Learning Dataset of Molecules, containing molecular compounds with measurements of activity against a variety of protein targets. The dataset is presented with a model evaluation benchmark which aims to drive few-shot learning research in the domain of molecules and graph-structured data.

C0nc/GraphMAE

GraphMAE: Self-Supervised Masked Graph Autoencoders in KDD'22

C0nc/GraphMAE2

GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner in WWW'23

C0nc/GSSG

Gene Set + S2G strategy annotations analyzed for disease architecture

C0nc/integration

C0nc/milopy

Python implementation of Milo for differential abundance testing on KNN graph

C0nc/MTGCN

C0nc/MVTCAE

C0nc/scInformer

C0nc/SL