Reinforcement Learning for Molecular Design Guided by Quantum Mechanics

PhD Thesis

A unifying representation of single cell expression profiles that quantifies similarity between expression states and generalizes to represent new studies without additional training.

Epigenomics Program pipeline to analyze SHARE-seq data.

Interactive 2D scatter plot widget for Jupyter Lab and Notebook. Scales to millions of points!

🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch and FLAX.

Single sample Gene Set Enrichment analysis (ssGSEA) and PTM Enrichment Analysis (PTM-SEA)

Chai-1, SOTA model for biomolecular structure prediction

Deep count autoencoder for denoising scRNA-seq data

Senescent Cell Identification

Python bindings for hictk: read and write .cool and .hic files directly from Python

LoRNA-SH: A long-context RNA foundation model for predicting transcriptome architecture. Pre-processing and downstream analysis scripts.

Sequential Optimal Experimental Design of Perturbation Screens Guided by Multimodal Priors

Python library for array programming on biological datasets. Documentation available at: https://bionumpy.github.io/bionumpy/

Quantifying metabolism activity at the single-cell resolution

Gene Set Enrichment Analysis in Python

MAGIC (Markov Affinity-based Graph Imputation of Cells), is a method for imputing missing values restoring structure of large biological datasets.

TorchCFM: a Conditional Flow Matching library

Single-Cell (Perturbation) Model Library

Implementation of Autoregressive Diffusion in Pytorch

Implementation of Denoising Diffusion Probabilistic Model in Pytorch

Implementation of a framework for Genie2 in Pytorch

MACS -- Model-based Analysis of ChIP-Seq

R toolkit for inference, visualization and analysis of cell-cell communication from single-cell and spatially resolved transcriptomics

Python package to find communication-driven intercellular flows from single-cell RNA-sequencing and spatial transcriptomics data.