Divi Cornec

BiomiX 3.0: A user-friendly platform for democratized multi-omics integration with graph-based learning.

BiomiX 3.0 is a multi-omics integration platform that introduces PRAMIGO, a supervised heterogeneous graph transformer, and benchmarks five methods on CLL and PTB datasets, showing that no single method captures all biologically relevant signals.

Interferon-α-Driven Stratification of B Cell Reveals Metabolic Reprogramming of Double Negative, Naive and Transitional cell subsets and Refines Molecular Classification in Söjgren's Disease.

This paper identifies an IFN-α signature in B cells of Sjögren's disease patients, revealing metabolic reprogramming and refining molecular classification.

FloREN: Decoding Immune Regulatory Networks through Interpretable Graph Transformer Patient Representations.

FloREN improves patient-level sample stratification and biomarker discovery by modeling single-cell data as an interpretable graph transformer network for immune regulatory analysis.

The SjD Map: An interactive pathway tour into Sjogren's disease signalling mechanisms

The paper develops a molecular interaction map of Sjogren's disease using transcriptomic data and literature, integrating pathway analysis and experimental data for therapeutic insights.

BiomiX-Driven Multi-Omics Integration of PRECISESADS Data Reveals Lysophosphatidic Acid and Metabolic Pathway Signatures in B Cells and Immune Macroenvironment in Sjogren's Disease

This study integrates multi-omics data from Sjogren's disease patients to identify LPA and metabolic pathway signatures in B cells and the immune microenvironment.

BiomiX, a User-Friendly Bioinformatic Tool for Automatized Multiomics Data Analysis and Integration

BiomiX automates multiomics data analysis and integration, enabling efficient comparison of two cohorts through MOFA and providing interpretable results using literature and clinical data.

Integrative Analysis Reveals a Molecular Stratification of Systemic Autoimmune Diseases

The paper identifies four molecular clusters in systemic autoimmune diseases using transcriptome and methylome data, showing three stable aberrant patterns and one non-specific cluster.