Non-parametric density inference for single-cell analysis.

🔥Highlighting the top ML papers every week.

RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding.

Inferring multi-modality dynamics for single cell genomics data

Bayesian model for RNA velocity estimation of periodic manifolds

Tutorials on using latest dynamo package

Python package for analysis of multiomic single cell RNA-seq and ATAC-seq.

RNA velocity validation

Variational Estimation of Latent Velocity from Expression with Time-resolution

Yanshi Hu's personal website

Multi-omic velocity inference

Inclusive model of expression dynamics with conventional or metabolic labeling based scRNA-seq / multiomics, vector field reconstruction and differential geometry analyses

PHOENIX: a tool to infer biologically informed NeuralODEs describing genome-wide regulatory dynamics

Deep probabilistic analysis of single-cell and spatial omics data

Predict RNA velocity through deep learning

A deep learning architecture for robust inference and accurate prediction of cellular dynamics

Entropy, mutual information and higher order measures from information theory, with various estimators and discretisation methods.

Toolbox - generic utilities for data processing (e.g., parsing, proximity, guild scoring, etc...)

Paper list for equivariant neural network

This package contains deep learning models and related scripts for RoseTTAFold

Repository for benchmarking graph neural networks

OBO-formatted ontologies → networkx (Python 3)

User-friendly extensions to the Disease Ontology

PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations

Graph Neural Network Library for PyTorch

Python package built to ease deep learning on graph, on top of existing DL frameworks.

Code and resources on scalable and efficient Graph Neural Networks