Emanuele de Rinaldis

The paper constructs a multi-ancestry single-cell eQTL map for SLE, identifying colocalized eQTLs with GWAS loci and highlighting cell-type-specific regulatory mechanisms.

This study identifies genetic risk factors associated with severe psoriasis and shows that polygenic risk scores can predict severe disease outcomes better than traditional risk factors.

The paper uses single-cell transcriptomics and neural network classifiers to identify B cell signaling as critical for NK cell expansion in allogenic therapy.

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 deep learning to predict YAP1/TEAD activity from histology slides, linking histological patterns to disease aggressiveness in cancer patients.

The paper performs a meta-analysis of scRNA-seq data to identify disease-specific and shared chemokine-based cell communication patterns in immune diseases.

This study identifies genetic risk loci and candidate genes for inflammatory polyneuropathy and CIDP through GWAS and functional analyses.

This study investigates how scRNA-seq data can predict the clinical success of drug targets by analyzing cell type and disease-specific expression patterns.

This study conducts a GWAS to analyze acne severity, identifying two new loci and six novel genes associated with severity, while showing limited overlap with known acne risk loci.

This study identifies therapeutic targets by integrating GWAS with multi-omics data, finding enriched causal genes and novel associations like IL6ST in polymyalgia rheumatica.

The paper introduces GENIX, a platform for identifying perturbation-relevant genes using gene association networks, applied to influenza vaccine responses in COVID-19 recovered patients.

This study identifies epigenetic differences in SLE linked to genetics, transcription, and serology, revealing novel drug targets and pathways.

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

This study identifies de novo mutations and rare variant burdens in SLE candidate genes, supporting their role in disease pathogenesis.