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DNADNA - Deep Neural Architectures for DNA

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.

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Research activities

  CHARPIAT Guillaume
  JAY Flora
  BRAY Erik
  SANCHEZ Théophile
  CURY Jean
  JOBIC Pierre

  Learning and Optimization
  Software development
Software & patents
The C++ Bulk Synchronous Parallelism Library

A prototype to automate semantic mappings between taxonomies

Procédé pour l’extinction de routeurs dans un réseau de communications et routeur mettant en œuvre ce procédé