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 <carl.nettelblad@it.uu.se> |
Affiliation: |
Uppsala universitet |
Duration: |
2024-04-01 – 2024-10-01 |
Classification: |
40402 |
Homepage: |
https://github.com/kausmees/GenoCAE |
Keywords: |
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Abstract
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.