PRnet is a flexible and scalable perturbation-conditioned generative model predicting transcriptional responses to unseen complex perturbations at bulk and single-cell levels.

A general, evolvable, and distributed agent framework & harness for data science.

Code relevant for the analysis in Maziak et al.

Bizard: A Biomedical Visualization Atlas. https://openbiox.github.io/Bizard/

Your own personal AI assistant. Any OS. Any Platform. The lobster way. 🦞

R toolkit for enhanced downstream analysis of Taiji

A Multi-modal System for Automated Mining of Protein-Ligand Bioactivity Data from Literature

Research code accompanying AlphaGenome

State is a machine learning model that predicts cellular perturbation response across diverse contexts

🧬 Generative modeling of regulatory DNA sequences with diffusion probabilistic models 💨

Single cell perturbation prediction

H2M is a Python pipeline for precise modeling of human variants in the mouse genome.

Genotyping of paralog specific gene types

A high-performance modification detection tool in Rust

isoviz is a package that allows for simplified junction to transcript level visualization with Cas13 gRNA design capability.

An ultra-fast all-in-one FASTQ preprocessor (QC/adapters/trimming/filtering/splitting/merging...)

wrapper scripts for convenient STARsolo processing of 10X and other scRNA-seq

A wrapper for the kallisto | bustools workflow for single-cell RNA-seq pre-processing

A deep learning framework for essential gene prediction

An interpretable deep learning model for cancer classification using high-dimensional omics data

identify pre-miRNAs using CNN models trained on huamn pre-miRNAs dataset

DeepG4: A deep learning approach to predict active G-quadruplexes

Code for analysis of CRAFT-seq data