Speaker
Description
Biomedical data poses multiple hard challenges that break conventional machine learning assumptions. In this talk, I will highlight the need to transcend our prevalent machine learning paradigm and methods to enable them to become the driving force of new scientific discoveries. I will present machine learning methods that have the ability to bridge heterogeneity of individual biological datasets by transferring knowledge across datasets with a unique ability to discover novel, previously uncharacterized phenomena. I will discuss the findings and impact these methods have for annotating comprehensive single-cell atlas datasets and discovery of novel cell types.
The papers my talk was mostly based on are:
https://www.nature.com/articles/s41592-020-00979-3
https://www.nature.com/articles/s41592-022-01651-8
https://arxiv.org/pdf/2102.03526.pdf
https://www.biorxiv.org/content/10.1101/2023.02.03.526939v1.full.pdf
