kexinhuang12345

kexinhuang12345's Stars

• facebookresearch/swav

  PyTorch implementation of SwAV https//arxiv.org/abs/2006.09882

  Language:Python2k41116283

• divelab/DIG

  A library for graph deep learning research

  Language:Python1.9k31211284

• igraph/igraph

  Library for the analysis of networks

  Language:C1.8k821.9k407

• facebookresearch/DomainBed

  DomainBed is a suite to test domain generalization algorithms

  Language:Python1.4k29106300

• KaiyangZhou/Dassl.pytorch

  A PyTorch toolbox for domain generalization, domain adaptation and semi-supervised learning.

  Language:Python1.2k1757172

• nglviewer/nglview

  Jupyter widget to interactively view molecular structures and trajectories

  Language:Jupyter Notebook82721500134

• benedekrozemberczki/SimGNN

  A PyTorch implementation of "SimGNN: A Neural Network Approach to Fast Graph Similarity Computation" (WSDM 2019).

  Language:Python7711147147

• zh217/torch-dct

  DCT (discrete cosine transform) functions for pytorch

  Language:Python58772172

• zqfang/GSEApy

  Gene Set Enrichment Analysis in Python

  Language:Python58311207119

• domainadaptation/salad

  A toolbox for domain adaptation and semi-supervised learning. Contributions welcome.

  Language:HTML334153642

• tencent-ailab/grover

  This is a Pytorch implementation of the paper: Self-Supervised Graph Transformer on Large-Scale Molecular Data

  Language:Python33371870

• gtegner/mine-pytorch

  Mutual Information Neural Estimation in Pytorch

  Language:Jupyter Notebook3037657

• facebookresearch/CPA

  The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level. CPA performs OOD predictions of unseen combinations of drugs, learns interpretable embeddings, estimates dose-response curves, and provides uncertainty estimates.

  Language:Jupyter Notebook178141146

• janfreyberg/pytorch-revgrad

  A minimal pytorch package implementing a gradient reversal layer.

  Language:Python1573414

• kundajelab/bpnet

  Toolkit to train base-resolution deep neural networks on functional genomics data and to interpret them

  Language:Jupyter Notebook145153935

• dhimmel/obonet

  OBO-formatted ontologies → networkx (Python 3)

  Language:Python13691828

• idekerlab/DrugCell

  A visible neural network model for drug response prediction

  Language:Python133112555

• idekerlab/DCell

  DCell browser and gene deletion simulator

  Language:JavaScript121193834

• claying/OTK

  A Pytorch implementation of the optimal transport kernel embedding

  Language:Python1143313

• ArnovanHilten/GenNet

  Framework for Interpretable Neural Networks

  Language:Jupyter Notebook9661513

• eth-sri/dl2

  DL2 is a framework that allows training neural networks with logical constraints over numerical values in the network (e.g. inputs, outputs, weights) and to query networks for inputs fulfilling a logical formula.

  Language:Python849517

• yueyu1030/SumGNN

  [Bioinformatics 2021] This is the repo for the paper `SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization'.

  Language:Python8401320

• dahjan/DMS_opt

  Language:Python675524

• snap-stanford/multiscale-interactome

  Language:Python644013

• RuishanLiu/TrialPathfinder

  Language:Jupyter Notebook573126

• MichSchli/GraphMask

  Official implementation of GraphMask as presented in our paper Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking.

  Language:Python424310

• maximilianh/crisporPaper

  Code for the CRISPOR article, all data and code to create figures and analysis

  Language:Python4110220

• Su-informatics-lab/DSTG

  Deconvoluting Spatial Transcriptomics Data through Graph-based Artificial Intelligence

  Language:Python3642010

• kundajelab/coessentiality

  Companion to "A genome-wide almanac of co-essential modules assigns function to uncharacterized genes" (https://doi.org/10.1101/827071)

  Language:Python27639

• i-Molecule/bitenet

  Spatiotemporal identification of druggable binding sites using deep learning

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