Deep learning models for modelling genetic variation
Title: Deep learning models for modelling genetic variation
DNr: Berzelius-2024-121
Project Type: LiU Berzelius
Principal Investigator: Carl Nettelblad <>
Affiliation: Uppsala universitet
Duration: 2024-04-01 – 2024-10-01
Classification: 40402


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. Weäre currently exploring contrastive learning and other recent self-supervised approaches for low-dimensional embeddings of this kind of data. We are also considering diffusion-based models in this context.