Rosalba Giugno

CRISPR-HAWK identifies genetic variants affecting CRISPR guide performance across diverse populations, revealing that 82.5% of therapeutic guides are impacted by variants.

WaveDNA improves TFBS prediction by using wavelet transforms to convert DNA sequences into images, enabling efficient and interpretable deep learning with fewer parameters and resources.

This study benchmarks PWM and SVM-based models for TFBS prediction using synthetic and biological data, highlighting their strengths and limitations under different conditions.

EasyCircR detects and reconstructs circRNA-miRNA-mRNA regulatory networks through post-transcriptional analysis and visualization.

This paper identifies ADARreg, an elncRNA that modulates BCR signaling and immune evasion in lymphoma by altering RNA stability, localization, and editing, providing a potential therapeutic target.

DiGAS improves genetic disease diagnosis by directly analyzing gene-level SNP significance without relying on GWAS, achieving high accuracy in Alzheimer's disease classification.

Esearch3D predicts active enhancers by propagating gene expression across 3D-genome networks, showing enrichment in enhancer-associated features.

The paper develops CRISPRme, a tool that integrates genetic diversity data to assess off-target risks of gene editing, finding allele-specific off-target effects in clinical gRNAs.

This study shows that activating PIEZO1 channels in microglia improves amyloid beta clearance in AD models by enhancing survival, phagocytosis, and lysosomal activity.

Stardust improves spatial transcriptomics clustering by integrating spatial and transcriptomic data through modularity optimization, achieving more stable and biologically coherent results.

GRAFIMO scans transcription factor binding sites in pangenome variation graphs, identifying variant-specific binding events that are missed or altered in reference genomes.

The paper proposes a multi-view workflow for integrating imaging and genetic data in Parkinson's Disease to identify potential genetic biomarkers.

This study identifies disease-specific gene expression and protein secretion patterns in olfactory mucosa cells from Alzheimer's patients, revealing potential diagnostic and mechanistic insights.