Fabian Kern

This paper uses VINE-seq to create a vascular transcriptomic atlas in Alzheimer's disease, revealing transcriptional changes in vascular cells and identifying angiopoietin signaling as a key dysregulated pathway.

This study uses spatial transcriptomics to compare aging and infection effects across multiple organs in mice, identifying shared gene expression patterns in immune pathways.

This study identifies SRF as a transcriptional regulator of cytoskeletal genes in oligodendrocytes essential for myelination during development.

This study identifies molecular foci of brain aging in mice using spatiotemporal RNA-seq, showing accelerated glial aging in white matter and differential gene expression linked to neurodegenerative diseases.

This study uses single-cell RNA-sequencing to analyze cell type-specific responses to young or aged blood in heterochronic parabiosis, identifying mitochondrial function as a key factor in rejuvenation.

The paper analyzes single-nucleus transcriptomes from brain and choroid plexus tissues of COVID-19 and influenza patients, identifying broad transcriptional dysregulation in multiple cell types and linking these changes to neurological symptoms.

This paper introduces Swarm Learning, a decentralized machine learning approach for disease classification using transcriptomic data, demonstrating its effectiveness in developing classifiers for COVID-19, tuberculosis, and leukemias while preserving privacy.

The paper identifies aging-associated differences in vesicle-bound and freely circulating small regulatory RNAs in mice, showing tRNA fragments and miRNAs strongly correlate with aging.

VINE-seq yields 143,793 single-nucleus transcriptomes of human brain vascular and perivascular cells; 30 of the top 45 Alzheimer's GWAS genes are expressed there, many microglial in mice.

The paper analyzes sncRNA-seq data from the PPMI cohort to identify miRNAs associated with Parkinson's disease progression and systemic inflammation.

miEAA 2.0 enhances miRNA enrichment analysis by supporting multiple species, adding new categories, and providing API access, with case studies in human and mouse data.

miRSwitch detects microRNA arm shifts and switches using sncRNA-seq data, providing analysis tools and a reference map of arm shift frequencies across tissues and species.

The paper improves gene expression prediction by integrating chromatin conformation data and identifies key transcription factors involved in enhancer-promoter loops.

The paper assesses miRNA expression changes in blood samples from endurance-trained individuals and identifies miR-532-5p as a potential marker for training performance and metabolic response.

The paper uses machine learning to analyze circulating microRNAs and finds they can distinguish Alzheimer's patients from controls with 87.6% accuracy.