Deep learning models for modelling genetic variation
||Deep learning models for modelling genetic variation|
||Carl Nettelblad <email@example.com>|
||2023-09-20 – 2024-04-01|
We are developing deep learning models based on autoencoder architectures for modelling genetic variation, as well as predicting traits of economic importance in plant and animal breeding applications.
Our deep learning genetics model was recently accepted in the genetics journal G3. There are currently only a few successful models for full genome models, with ours being one.
Specifically, we aim to apply our models to the NIAB Magic Wheat population http://mtweb.cs.ucl.ac.uk/mus/www/MAGICdiverse/, with a significant scaling in the number of genotype variants. We have exploed contrastive learning, but we now want to combine that with an embedding that also uses phenotype data, and not only the genotype data of the dataset.