Vincenzo Lagani

The human RNA-DNA interactome is cell type-specific and dynamic

The paper maps RNA-DNA interactions in 16 human cell types, showing cell-type-specific and dynamic networks that correlate with gene regulation and disease traits.

Stromal and Endothelial Transcriptional Changes during progression from MGUS to Myeloma and after Treatment Response

This study identifies transcriptional changes in stromal and endothelial cells during MGUS to myeloma progression and treatment response, revealing a tumor-permissive BME signature that is modulated by therapy.

Co-regulatory changes in Homo sapiens prefrontal cortex shape RNA-chromatin interactions

The paper maps RNA-chromatin interactions in human prefrontal cortex, revealing co-regulatory changes linked to brain evolution and disease pathways.

Bone Marrow Spatial Transcriptomics Reveals a Myeloma Cell Architecture with Dysfunctional T-Cell Distribution, Neutrophil Traps, and Inflammatory Signaling

This study uses spatial transcriptomics to analyze bone marrow in MM mouse models and patients, revealing altered T-cell distribution, neutrophil traps, and inflammatory signaling in MM.

Epigenetic Age Prediction Remains Stable Across Common Variants and Diverse Ancestries

This study evaluates the robustness of epigenetic clocks to common SNPs across diverse ancestries and finds they remain stable despite cSNP-CpG overlaps.

GeneSetCluster 2.0: a comprehensive toolset for summarizing and integrating gene-sets analysis

GeneSetCluster 2.0 improves gene-set analysis by reducing redundancies, enhancing computational efficiency, and providing accessible tools for both programmers and non-programmers.

Charting the single cell transcriptional landscape governing visual imprinting.

The paper identifies transcriptional changes in the avian brain during visual imprinting using single-nucleus RNA sequencing, revealing cell clusters and lncRNAs linked to memory formation.

A genome-wide, machine learning-guided exploration of the cis-regulatory code involved in neuronal differentiation

The paper develops a machine learning method to identify cis-regulatory element profiles and their sequence features during neuronal differentiation using single-cell ATAC-seq and RNA-seq data.

Unravelling the molecular activation of the reparative cardiac fibroblasts after myocardial infarction

The paper identifies molecular dynamics and a new marker, Aspn, for reparative cardiac fibroblasts in mice, pigs, and patients after myocardial infarction.

Mapping Disease Transitions from Premalignant, Asymptomatic to Advanced Myeloma through Integrative Epigenomic and Transcriptional Analyses

This study maps epigenetic and transcriptional changes in myeloma progression from precursor to advanced stages, identifying key transcription factors involved in disease development.

PrePR-CT: Predicting Perturbation Responses in Unseen Cell Types Using Cell-Type-Specific Graphs

This paper introduces a method to predict perturbation responses in unseen cell types using cell-type-specific graphs, showing improved performance over existing methods.

SpatialDiffusion: Predicting Spatial Transcriptomics with DenoisingDiffusion Probabilistic Models

This paper introduces a denoising diffusion model to predict and interpolate spatial transcriptomics data, enabling generative modeling of tissue slices.

scAEGAN: Unification of Single-Cell Genomics Data by Adversarial Learning of Latent Space Correspondences

scAEGAN unifies single-cell genomics data by adversarial learning of latent space correspondences, outperforming existing methods in integration and prediction tasks.

STATegra: Multi-omics data integration - A conceptual scheme with a bioinformatics pipeline

The paper introduces a conceptual framework and bioinformatics pipeline for multi-omics data integration, validated on TCGA datasets to enhance feature identification compared to single-omics analysis.

Latent Feature Representations for Human Gene Expression Data Improve Phenotypic Predictions

The paper constructs low-dimensional latent feature spaces of the human transcriptome using dimensionality reduction techniques and evaluates their ability to reconstruct data and improve phenotypic predictions.

Just Add Data: Automated Predictive Modeling and BioSignature Discovery

The paper introduces JADBIO, an AutoML system for omics data that produces predictive models and biosignatures, showing it can overcome overfitting in high-dimensional, low-sample data.

STATegra: a comprehensive multi-omics dataset of B-cell differentiation in mouse

The paper presents STATegra, a multi-omics dataset of mouse B-cell differentiation combining 10 omics technologies to study dynamic cellular processes.

Predicting Causal Relationships from Biological Data: Applying Automated Casual Discovery on Mass Cytometry Data of Human Immune Cells

The paper applies causal discovery methods to mass cytometry data of human immune cells to predict causal relationships between signaling proteins and their responses to perturbations.