Antonio Federico

The paper presents an AI workflow for mapping Key Events to genes in Adverse Outcome Pathways, improving accuracy and reducing manual effort through semantic retrieval, LLM refinement, and expert curation.

MUUMI integrates multi-omics data using statistical meta-analysis and network-based methods to identify robust molecular signatures and interactomes.

OpTiles dynamically defines tiling windows for methylation data based on CpG distribution and variability, improving reliability and annotation of targeted methylation datasets.

This paper curates and harmonizes transcriptomics datasets from interstitial lung disease patients to improve data FAIRness and enable integrated systems biology approaches.

This study identifies 440 genes/proteins associated with atopic dermatitis severity through multi-omics analyses and highlights four with significant cross-evidence, including CEP85, which may contribute to severity via impaired phagocytosis.

This study uses RNA sequencing to analyze immune gene responses in macrophages exposed to bleomycin, identifying dose-dependent gene alterations and linking them to cytokine and proteinase activity.

This study investigates the immunomodulatory roles of platelet-derived extracellular vesicles by comparing their functional effects and omics profiles across different activation methods.

This study investigates how polarizing environments affect macrophage gene expression and DNA methylation, revealing long-term regulatory responses and common epigenetic changes across stimuli.

This paper develops a network-based toxicology approach to model nanomaterial hazard by linking molecular changes to adverse outcomes in biological systems.

This paper maps disease relationships using six data dimensions to reveal novel insights beyond traditional classifications, identifying shared features between Type 2 diabetes and Alzheimer's disease.

This study integrates public transcriptomic data with network analysis to identify molecular features of psoriasis, revealing key genes and networks involved in the disease.

SFMetaDB curates RNA-Seq datasets for mouse splicing factors, revealing a gap in functional studies and highlighting understudied factors.