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alphafold

Open source code for AlphaFold.

anomalydetectioncvpr2018

antialiased-cnns

Antialiasing cnns to improve stability and accuracy. In ICML 2019.

autokeras

Accessible AutoML for deep learning.

awesome-graph-classification

A collection of important graph embedding, classification and representation learning papers with implementations.

bert

TensorFlow code and pre-trained models for BERT

cancer-data

TCGA data acquisition and processing for Project Cognoma

clintrials

Cataloging pharmacotherapies in clinical trial from ClinicalTrials.gov

cognoma

Putting machine learning in the hands of cancer biologists

cryodrgn

Neural networks for cryo-EM reconstruction

dain

Depth-Aware Video Frame Interpolation (CVPR 2019)

datasciencecoursera

datasharing

The Leek group guide to data sharing

deep-review

A collaboratively written review paper on deep learning, genomics, and precision medicine

deepvariant

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.

deviation-network

Deep anomaly detection with deviation networks (KDD19)

drugbank

User-friendly extensions of the DrugBank database

edugen-notebooks

edvr

Winning Solution in NTIRE19 Challenges on Video Restoration and Enhancement (CVPR19 Workshops) - Video Restoration with Enhanced Deformable Convolutional Networks

esrgan

ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution (Third Region)

esrgan-keras

Recurring the ESRGAN with Keras

faceswap

Non official project based on original /r/Deepfakes thread. Many thanks to him!

falsr

Fast, Accurate and Lightweight Super-Resolution models

fast-autoaugment

Official Implementation of 'Fast AutoAugment' in PyTorch.

gene-ontology

User-friendly Gene Ontology annotations

google-research

Google AI Research

gpt-3

GPT-3: Language Models are Few-Shot Learners

hetionet

Hetionet: an integrative network of disease

hetnetpy

Hetnets in Python (relocated from dhimmel/hetio)

hsdn

Analysis of the human symptoms–disease network