Roman Martin

gLinDA analyzes microbiome data using swarm learning to detect differential abundance without sharing raw patient data.

Ensemble-GNN enables federated, ensemble-based graph neural networks for disease module discovery using patient-specific PPI networks and omics data.

ODNA uses machine learning to classify organellar DNA sequences in genome assemblies, achieving high accuracy and outperforming existing methods.

MOSGA 2 provides advanced genomic analysis tools for annotation, validation, and comparative genomics across multiple datasets.

The paper introduces γBOriS, a motif-based machine learning tool for identifying oriC sequences in Gammaproteobacteria, and presents BOriS DB, a database containing 25,827 oriC sequences from 1,217 species.

The paper presents four high-quality draft genome assemblies of Cafeteria roenbergensis, enabling evolutionary analysis of heterotrophic protists.