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accelerate

🚀 A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision

alphafold

Open source code for AlphaFold.

ardca.jl

Autoregressive networks for protein

batchbald_redux

Reusable BatchBALD implementation

bayesian-torch

A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch

biopython

Official git repository for Biopython (originally converted from CVS)

byprot

cagi5_mpra_prediction

Code for entry into cagi 5 saturation mutagenesis challenge (predicting consequences of point mutations in promoters/enhancers on gene expression in MPRA)

colabfold

Making Protein folding accessible to all!

combining-evolutionary-and-assay-labelled-data

convert_torch_to_pytorch

Convert torch t7 model to pytorch model and source.

cryptic

danq

A hybrid convolutional and recurrent neural network for predicting the function of DNA sequences

deep-active-learning

An implementation of the state-of-the-art Deep Active Learning algorithms

deep-protein-generation

django-celery

Old Celery integration project for Django

dsmbind

edice

Epigenomic Data Imputation via Contextualised Embeddings

edice-pytorch

encode-imputation

Code for entry into ENCODE imputation challenge (team imp)

esm

Evolutionary Scale Modeling (esm): Pretrained language models for proteins

evcouplings

Evolutionary couplings from protein and RNA sequence alignments

fitness_data

Protein fitness datasets

flexs

Fitness landscape exploration sandbox for biological sequence design.

gameoflife

Python implementation of conway's game of life based on Stanford CS221 tutorial

gaussdca.jl

Multivariate Gaussian Direct Coupling Analysis for residue contact prediction in protein families - Julia module

gwg_release

Official release of code for "Oops I Took A Gradient: Scalable Sampling for Discrete Distributions"

keras

Deep Learning for humans

kotsu

Lightweight framework for structured and repeatable model validation

laplace

Laplace approximations for Deep Learning.