Véra Pancaldi

This paper develops a multiscale framework linking pharmacokinetics and Boolean models to study ibrutinib resistance in CLL, identifying key regulons and synergistic targets.

This study identifies PTBP1-CDC42 splicing regulation as a critical factor in AML growth and venetoclax sensitivity, suggesting a therapeutic combination strategy.

CellTFusion identifies functional multicellular states from bulk RNA-seq data by integrating cell type deconvolution and transcriptional regulatory network analysis, revealing TME programs linked to immunotherapy responses.

The paper examines the spatial organization of dendritic cell subsets in NSCLC patients treated with anti-PD1 and finds that cDC1-driven patterns correlate with prolonged ICB response.

The paper introduces pipeML, a framework for leakage-free machine learning in omics data by enabling fold-aware cross-validation, improving model evaluation accuracy.

mosna analyzes spatial omics data to identify cell interactions predictive of immunotherapy response and survival in cancer.

The paper identifies viral oncogenic potential by analyzing protein-protein interaction networks to uncover mechanisms linking viral infections to cancer development.

PhysiGym integrates PhysiCell with Gymnasium to enable reinforcement learning control of in silico biological processes, demonstrating tumor elimination in a case study.

This study develops an in vitro 3D tumor spheroid model to target pro-tumoral macrophages with antibodies, showing effective killing even in spheroid centers.

This study identifies epigenetic regulatory elements linked to breast cancer by analyzing multi-omics data and reveals potential biomarkers and therapeutic targets.

multideconv reduces redundancy and improves accuracy in cell type deconvolution by integrating results from multiple methods and signatures.

The paper examines gut microbial ecosystems across different metabolic and obesity phenotypes, finding that metabolic health, not obesity, drives microbial connectivity and functional cohesion.

The paper uses time-series RNA-Seq and gene regulatory network inference to study how CLL cells respond to immune signals in vitro, revealing dynamic regulatory mechanisms and patient-specific gene modules.

This study shows CLL cells acquire FOLR2 from nurse-like cells via trogocytosis, enhancing their survival and proliferation in vitro.

The paper develops an agent-based model to simulate monocyte differentiation into nurse-like cells in chronic lymphocytic leukemia and identifies patient-specific macrophage phenotypes linked to cancer survival.

The paper investigates Drosophila insulators' role in 3D chromatin organization, finding that non-border preferential interactions occur before TADs and transcription.

The paper introduces tysserand, a Python library for reconstructing spatial networks from spatially resolved omics data, with tools for method integration, parameter selection, and network cleaning.

The paper introduces new gene expression and DNA methylation signatures for tumor immune microenvironment profiling, showing improved performance in predicting immunotherapy response.

The paper builds a Boolean model to simulate macrophage polarization in an in-vitro chronic lymphocytic leukaemia model, revealing the formation of tumour-associated macrophages.

The paper introduces two tools for analyzing chromatin networks using epigenomic data in hematopoietic cells.

The paper uses matrix factorization to analyze molecular mechanisms behind the inverse comorbidity between Alzheimer’s disease and lung cancer, identifying new candidate factors and subtype-specific mechanisms.

The paper investigates how 3D chromatin structure and chromatin environment influence DNA replication origin activation efficiency in mouse embryonic stem cells under stress and control conditions.

ChiCMaxima detects and visualizes chromatin loops in Capture Hi-C data using local maxima and a background model, showing improved robustness over existing tools.

The paper compares gene expression profiles of ASD and 22 cancer types, finding shared and opposing deregulations in specific cancers and common biological pathways.

The paper constructs a disease interaction network from patient molecular profiles to uncover molecular mechanisms of comorbidity and identify associated genes and drugs.

This study compares the epigenomes of neutrophils and monocytes to identify regulatory elements and chromatin states, revealing differences in enhancer activity and DNA methylation.

The paper develops a bioinformatic workflow to classify blood epigenomes by cell type and identifies chromatin determinant regions linked to cell identity and disease.

The paper measures and compares transcriptional and epigenetic variability across immune cell types and finds neutrophils exhibit higher variability linked to immune pathways and environmental exposure.