Dalia Mizikovsky

This study identifies latent regulatory programs from disease-associated variants to reveal biological mechanisms and clinical subtypes in complex diseases.

This study shows glycaemic variability, not just hyperglycaemia, increases heart muscle dysfunction and myocardial injury risk in diabetes using genetic and experimental models.

This study compares sixteen iPSC-CM differentiation protocols to identify how protocol-dependent cell states affect disease modeling capacity for cardiovascular conditions.

This paper introduces an unsupervised framework to identify genetic subtypes of disease by clustering variant co-occurrence, applied to type 2 diabetes and asthma without using phenotype data.

The paper identifies regions of the human genome under cellular constraint by analyzing chromatin accessibility across cell types, showing their predictive power for functional loci and applications in variant identification and drug discovery.

This paper introduces a consensus clustering method to identify robust signals from high-throughput screens by analyzing multi-scale data organization in cancer cell lines and bacterium models.

The paper introduces TRIAGE-Cluster and TRIAGE-ParseR to define cell diversity in scRNA-seq data using epigenetic data as a biological anchor.

The paper creates an iPSC differentiation atlas and identifies TMEM88 as a regulator of mesendodermal development and blood pressure.

This study introduces UnTANGLeD, an unsupervised method that organizes genes into programs based on gene-trait associations, revealing 173 clusters with distinct biological functions and disease relevance.

This study identifies HOPX as a molecular switch regulating cardiomyocyte progenitor and maturation gene programs, linking it to cardiac function and genetic variation.