DNADNA - Deep Neural Architectures for DNA
Date of the last release: 20 June 2020
Person in charge :
JAY Flora
DNADNA is a package for deep learning inference in population genetics. DNADNA provides utility functions to improve development of neural networks for population genetics and is currently based on PyTorch.
In particular, it already implements several neural networks that allow inferring demographic and adaptive history from genetic data. Pre-trained networks can be used directly on real/simulated genetic polymorphism data for prediction. Implemented networks can also be optimized based on user-specified training sets and/or tasks. Finally, any user can implement new architectures and tasks, while benefiting from DNADNA input/output, network optimization, and test environment.
More information: https://gitlab.com/mlgenetics/dnadna
Software
Research activities
Members
CHARPIAT Guillaume JAY Flora BRAY Erik SANCHEZ Théophile CURY Jean JOBIC Pierre
Group
Bioinformatics Learning and Optimization Software development
