eachanjohnson
Group leader at @FrancisCrickInstitute Systems Chemical Biology of Infection and Resistance lab
@FrancisCrickInstituteLondon, UK
eachanjohnson's Stars
shap/shap
A game theoretic approach to explain the output of any machine learning model.
chaidiscovery/chai-lab
Chai-1, SOTA model for biomolecular structure prediction
jwohlwend/boltz
Official repository for the Boltz-1 biomolecular interaction model
recursionpharma/mole_public
Recursion's molecular foundation model
google-deepmind/alphafold3
AlphaFold 3 inference pipeline.
siddharthab/UMICollapse
Accelerating the deduplication and collapsing process for reads with Unique Molecular Identifiers (UMI). Heavily optimized for scalability and orders of magnitude faster than a previous tool.
flairNLP/flair
A very simple framework for state-of-the-art Natural Language Processing (NLP)
gradio-app/gradio
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
cytoscape/ipycytoscape
A Cytoscape Jupyter widget
SNU-CSSB/RF2-Lite
ablab/IsoQuant
Transcript discovery and quantification with long RNA reads (Nanopores and PacBio)
mmp2/megaman
megaman: Manifold Learning for Millions of Points
DLR-RM/stable-baselines3
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
kelwyres/Bactabolize
jhkorhonen/MOODS
MOODS: Motif Occurrence Detection Suite
npdeloss/meirlop
Motif Enrichment In Ranked Lists Of Peaks
sanger-pathogens/Roary
Rapid large-scale prokaryote pan genome analysis
kblin/ncbi-acc-download
Download files from NCBI Entrez by accession
ThomasCMcLean/LazyAF
Code for LazyAF pipeline
pyg-team/pytorch_geometric
Graph Neural Network Library for PyTorch
ncbi/datasets
NCBI Datasets is a new resource that lets you easily gather data from across NCBI databases.
Opentrons/opentrons
Software for writing protocols and running them on the Opentrons Flex and Opentrons OT-2
RosettaCommons/RoseTTAFold
This package contains deep learning models and related scripts for RoseTTAFold
MatthijsHak/MetalDock
Dock organometallic compounds to proteins/DNA/biomolecules
aqlaboratory/openfold
Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2
xqding/DCA
baker-laboratory/RoseTTAFold-All-Atom
patrickbryant1/SpeedPPI
Rapid protein-protein interaction network creation from multiple sequence alignments with Deep Learning
mbhall88/NanoVarBench
Evaluating Nanopore-based bacterial variant calling
IBM/topography-searcher
This Python package is designed for mapping the solution space of machine learning models. An understanding of the organisation of the solution space can answer important questions about the reproducibility, explainability and performance of ML methods.