Deep integrative omic auto-encoder analysis
||Deep integrative omic auto-encoder analysis|
||Mika Gustafsson <firstname.lastname@example.org>|
||2021-08-18 – 2021-12-01|
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.