Danai Vagiaki

This paper introduces LIVI, a deep learning framework for mapping trans-eQTLs at single-cell resolution, and shows it detects more trans-eQTLs than existing methods and identifies cell types and genes involved in autoimmune disease risk.

The paper develops a Lifestyle Factor Ontology and annotated corpus for detecting lifestyle factors in biomedical texts, achieving 76% F-score with a transformer-based NER system.

CellRegMap identifies context-specific regulatory variants using scRNA-seq by analyzing allelic effects across cell types and states.