Tobias Fehlmann

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.

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 creates a comprehensive mouse tissue atlas of small non-coding RNAs, revealing tissue-specific and sex-specific expression patterns and linking ncRNAs to cell types in the brain.

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.

The paper analyzes aging dynamics across 17 organs and plasma proteomics in mice, revealing consistent gene expression trajectories with organ-specific timing and amplitude.