Mattia Tomasoni

NOODAI: A webserver for network-oriented multi-omics data analysis and integration pipeline

NOODAI is a webserver that integrates multi-omics data through network analysis to identify disease-relevant modules and provide analytical outputs.

Comparing tangible retinal image characteristics with deep learning features reveals their complementarity for gene association and disease prediction

Phenotypic and Genetic Characteristics of Retinal Vascular Parameters and their Association with Diseases

The paper analyzes retinal vascular phenotypes from fundus images and their genetic and disease associations using UK Biobank data, finding novel links between vascular features and health outcomes.

GWAS of Retinal Vessel Tortuosity Identifies 173 Novel Loci Revealing Genes and Pathways Associated with Vascular Pathomechanics and Cardiometabolic Diseases

This study performs a GWAS on retinal vessel tortuosity, identifying 173 novel loci and linking them to vascular pathomechanics and cardiometabolic diseases.

MONET: a toolbox integrating top-performing methods for network modularisation

MONET provides a unified toolbox for disease module identification in molecular networks using top-performing methods from the DMI DREAM Challenge.

Assessment of network module identification across complex diseases

The paper evaluates module identification methods across disease-relevant networks using GWAS data to identify top-performing algorithms and disease-associated modules.

Automated analysis of large-scale NMR data generates metabolomic signatures and links them to candidate metabolites

The paper evaluates three NMR analysis tools for metabolite identification in human urine, finding ISA and ACP robustly identify metabolites while PCA fails.

PhenoMeNal: Processing and analysis of Metabolomics data in the Cloud

PhenoMeNal provides a cloud-based platform for metabolomics data analysis, integrating tools and workflows to enable reproducible and scalable research.