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coffee19850519 / scPEFT
Forked from SELECT-FROM/scPEFTofficial repo for scPEFT
A web-based framework to visualize dynamic networks in real-time.
Open source code for AlphaFold 2.
Must-read papers on graph neural networks (GNN)
A repository of pretty cool datasets that I collected for network science and machine learning research.
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
A deep-learning framework for characterizing and visualizing tissue architecture from spatially resolved transcriptomics
A standalone component to provide mappings between protein sequence positions and PDB 3D protein structure models
A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training
Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.
Code for "Heterogeneous Graph Transformer" (WWW'20), which is based on pytorch_geometric
The need to understand cell developmental processes has spawned a plethora of computational methods for discovering hierarchies from scRNAseq data. However, existing techniques are based on Euclide…
Single-cell analysis in Python. Scales to >100M cells.
Reproducing the experiments of the paper "Deep generative modeling for single-cell transcriptomics"
Proof-of-concept for reasoning over the SemMedDB knowledge base, using miniKanren + heuristics + indexing.
Subpopulation detection in high-dimensional single-cell data
Graph Classification with Graph Convolutional Networks in PyTorch (NeurIPS 2018 Workshop)
Code for reproducing results in GraphMix paper
TensorFlow implementations of Graph Neural Networks
Effector and Perturbation Estimation Engine (EPEE) conducts differential analysis of transcription factor activity from gene expression data.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
Source code for "Learning protein sequence embeddings using information from structure" - ICLR 2019
深度学习入门教程, 优秀文章, Deep Learning Tutorial