Deep integrative omic auto-encoder analysis
Title: |
Deep integrative omic auto-encoder analysis |
DNr: |
Berzelius-2021-26 |
Project Type: |
LiU Berzelius |
Principal Investigator: |
Mika Gustafsson <mika.gustafsson@liu.se> |
Affiliation: |
Linköpings universitet |
Duration: |
2021-08-18 – 2021-12-01 |
Classification: |
10203 |
Homepage: |
https://gitlab.com/Gustafsson-lab |
Keywords: |
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Abstract
We recently created a deep auto-encoder by analyzing ~70,000 RNA-seq samples and developed a new reverse-training method to identify functionally similar and potential disease-causing genes (Dwivedi et al Nature Communications 2020). The last 1,5 years we have analyzed DNA genomics and epigenomics (methylation) using the largest repositories (UKBiobank ~500,000 samples). Our goal is to create a deep data-driven multi-omics autoencoder that also includes prior biological knowledge and can be re-used to minimally represent the core data in smaller clinical studies for multiple purposes and be used for identification of disease-causing mechanisms.