# Summary
Personalized Medicine has the goal to improve the efficiency of medical treatments that is particularly import for deceases like cancer.
The basic premise of personalized medicine is that by understanding what distinguishes different patients from one another, we can design patience specific treatment plans that are more effective then giving the same treatment to everyone.
These patience differences can be studied from different perspectives, like a patience gender, their age, life style or more modern state of the art modalities, like a patience genome and epigenetic information.
Today on the show I am talking to Daria Romanovskaia an PhD Candidate in the Bocklab at the Research Center for Molecular Medicine in Vienna.
Daria describes how she applied machine learning methods on genetic and epigenetic data to identify patterns in gene regulation that can be used to create bio-makers that enable a targeted approach to decease treatment.
We will discuss how representation learning is used to perform dimensionality reduction and how clustering in combination with biological understanding of deceases can be used to identify relevant up and down regulated genes.
# TOC
00:00:00 Beginning
00:00:27 Episode Introduction
00:01:47 Guest Introduction
00:03:57 What is precision medicine?
00:06:35 Short introduction into genetics
00:13:25 High dimensional data and how to work around it
00:17:16 Variational Autoencoders
00:24:19 Topic Modelling
00:32:19 Analyzing embedding clusters
00:35:56 Combining data analysis and domain expertise
00:39:32 The human cell atlas
# Sponsors
Quantics: Supply Chain Planning for the new normal - the never normal - https://quantics.io/
Belichberg GmbH: We do digital transformations as your innovation partner - https://belichberg.com/
# References
https://www.linkedin.com/in/daria-romanovskaia-b37b2a8a/ - Daria Romanovskaia
https://www.bocklab.org/ - Bock research lab at CeMM
https://www.humancellatlas.org/ - Human Cell Atlas
https://cemm.at/research/groups/christoph-bock-group
