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DNADNA
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.

More information: https://gitlab.com/mlgenetics/dnadna





Research activities

Members
  CHARPIAT Guillaume
  BRAY Erik
  SANCHEZ Théophile
  CURY Jean
  JOBIC Pierre

Group
  Bioinformatics
  Learning and Optimization
  Software development
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