Omar Z. Kana

Multiregional single-cell profiling reveals shared and specialized cellular vulnerability in Alzheimer's disease

This study uses single-nucleus RNA-seq, ATAC-seq, and Multiome to analyze Alzheimer's disease across multiple brain regions, revealing shared and region-specific cellular vulnerabilities.

scVIP: personalized modeling of single-cell transcriptomes for developmental and disease phenotypes

scVIP models single-cell transcriptomes to predict phenotypes and infer trajectories in development and disease.

The Caudate Nucleus Exhibits Distinct Pathology and Cell Type-Specific Responses Across Alzheimer's Disease

The paper maps AD-related pathological changes in the caudate nucleus at cellular resolution, identifying distinct features and cell type-specific responses.

A cross-species spatial transcriptomic atlas of the human and non-human primate basal ganglia

This paper creates a cross-species spatial transcriptomic atlas of the basal ganglia in human, rhesus macaque, and common marmoset, revealing conserved molecular organization and cell type-specific compartments.

Integrated multimodal cell atlas of Alzheimer's disease

This study creates an integrated multimodal cell atlas of Alzheimer's disease by analyzing single nuclei transcriptomes, epigenomes, and spatial coordinates across AD stages.

A multiscale model of the mammalian liver circadian clock supports synchronization of autonomous oscillations by intercellular communication.

The paper develops a multiscale model of the mammalian liver circadian clock to show that intercellular communication is necessary for sustained circadian oscillations with a near 24-hour period.

Generative Modeling of Single Cell Gene Expression for Dose-Dependent Chemical Perturbations

This paper introduces scVIDR, a variational autoencoder for predicting dose-dependent gene expression in single cells across different cell types and tissues, showing improved performance over existing methods.

Prediction of mammalian tissue-specific CLOCK-BMAL1 binding to E-box motifs

The paper develops tissue-specific models to predict CLOCK-BMAL1 binding to E-box motifs using DNA sequence, shape, and histone modification features in mouse liver, heart, and kidney.

Predictive Models of Genome-wide Aryl Hydrocarbon Receptor DNA Binding Reveal Tissue Specific Binding Determinants

The paper develops predictive models to identify tissue-specific AhR binding determinants using machine learning on genomic features.