Daniela Bezdan

aiDIVA - Diagnostics of Rare Genetic Diseases Using Large Language Models

aiDIVA uses AI and LLMs to prioritize causal genetic variants in rare diseases, achieving high accuracy in identifying top candidates.

Spaceflight alters host-gut microbiota interactions

This study identifies spaceflight-induced changes in gut microbiota and host gene expression in mice, suggesting altered host-microbiome interactions contribute to spaceflight-related pathologies.

MapToCleave: high-throughput profiling of microRNA biogenesis in living cells

The paper introduces MapToCleave, a method to profile miRNA biogenesis in living cells, showing that stable lower basal stems enhance processing and expression across species.

Haplotype Diversity and Sequence Heterogeneity of Human Telomeres

This study uses long-read sequencing to analyze human telomere sequences, revealing sequence heterogeneity and non-canonical motifs.

Swarm Learning as a privacy-preserving machine learning approach for disease classification

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.

Shotgun Transcriptome and Isothermal Profiling of SARS-CoV-2 Infection Reveals Unique Host Responses, Viral Diversification, and Drug Interactions

This study uses rapid diagnostics and metatranscriptomics to analyze SARS-CoV-2 infection, revealing viral evolution, host responses, and drug interactions in NYC samples.

Tracking of antibiotic resistance transfer and rapid plasmid evolution in a hospital setting by Nanopore sequencing

The paper uses Nanopore sequencing to track antibiotic resistance plasmid evolution and transfer between bacterial species in a hospital setting, identifying plasmid fusion and gene rearrangements.

Novel Algorithms for the Taxonomic Classification of Metagenomic Linked Reads

KrakenLinked improves taxonomic classification of metagenomic linked-reads by combining tree pruning and taxa promotion algorithms, reducing false identifications compared to short reads.

Minerva: An Alignment and Reference Free Approach to Deconvolve Linked-Reads for Metagenomics

Minerva deconvolves barcoded reads in metagenomics to improve taxonomic assignments and sequence clustering without a reference genome.

Multi-omics approach identifies novel pathogen-derived prognostic biomarkers in patients with Pseudomonas aeruginosa bloodstream infection

The paper identifies novel pathogen-derived biomarkers associated with mortality in Pseudomonas aeruginosa bloodstream infections using a multi-omics approach.