Emmanuel Barillot

Specific dendritic cells spatial organization is associated to ICB Response in Non Small Cell Lung Cancer (NSCLC)

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.

HEDeST: An Integrative Approach to Enhance Spatial Transcriptomic Deconvolution with Histology

HEDeST improves spatial transcriptomic deconvolution by integrating histology features, achieving single-cell resolution and outperforming existing methods in simulated and real cancer datasets.

AstroLogics: A simulation-based analysis framework for monotonous Boolean model ensemble

AstroLogics analyzes Boolean model ensembles to identify differences in dynamics and regulation through simulation-based clustering and visualization.

NeKo: a tool for automatic network construction from prior knowledge

NeKo automates biological network construction by integrating molecular interactions from databases, enabling efficient network building for research applications.

sCellST: a Multiple Instance Learning approach to predict single-cell gene expression from H&E images using spatial transcriptomics

sCellST predicts single-cell gene expression from H&E images using spatial transcriptomics data, outperforming traditional methods in pancreatic cancer and detecting morphological differences in ovarian cancer.

MaBoSS for HPC environments: Implementations of the continuous time Boolean model simulator for large CPUclusters and GPU accelerators

The paper introduces parallel implementations of the continuous time Boolean model simulator for large CPU clusters and GPU accelerators to handle increased model complexity.

Identification of interpretable clusters and associated signatures in breast cancer single cell data: a topic modeling approach

This study uses topic modeling to cluster breast cancer single-cell data and identify gene signatures, improving interpretability and accuracy in distinguishing drug-sensitive and resistant cell types.

PhysiBoSS 2.0: a sustainable integration of stochastic Boolean and agent-based modelling frameworks

PhysiBoSS 2.0 integrates stochastic Boolean and agent-based models for multiscale cancer simulations, improving modularity and functionality.

Multiscale model of the different modes of cancer cell invasion

The paper presents a multiscale model of cancer cell invasion using PhysiBoSS, integrating spatial and intracellular processes to study migration modes and validate against experimental data.

A versatile and interoperable computational framework for the analysis and modeling of COVID-19 disease mechanisms

The paper presents a computational framework for analyzing SARS-CoV-2-host interactions to identify drug targets and model disease mechanisms.

UPMaBoSS: a novel framework for dynamic cell population modeling.

UPMaBoSS simulates cell population dynamics by extending MaBoSS to model interactions and death/division, revealing TNF-induced resistance mechanisms.

Patient-specific Boolean models of signaling networks guide personalized treatments

The paper develops patient-specific Boolean models of prostate cancer signaling networks to identify actionable targets for personalized treatment, finding dose-dependent effects of inhibitors on key targets.

Modeling progression of single cell populations through the cell cycle as a sequence of switches

The paper develops a simplified cell cycle model to analyze single-cell data and predict cell line doubling time from trajectory length.

Personalized logical models to investigate cancer response to BRAF treatments in melanomas and colorectal cancers

The paper develops personalized logical models to analyze cancer response to BRAF treatments, showing they correlate with experimental sensitivities using omics data without model training.

Exact calculation of stationary solution and parameter sensitivity analysis of stochastic continuous time Boolean models

The paper provides an exact method to calculate stationary solutions and analyze parameter sensitivity in stochastic Boolean models without Monte Carlo simulations.

Collective intelligence defines biological functions in Wikipedia as communities in the hidden protein connection network

The paper analyzes Wikipedia's protein functional network using the REGOMAX algorithm to identify evolving hidden communities of biological functions.

Molecular Inverse Comorbidity between Alzheimer’s disease and Lung Cancer: new insights from Matrix Factorization

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.

Metabolic and signalling network map integration: application to cross-talk studies and omics data analysis in cancer

The paper integrates cancer signalling and metabolic networks to enable multi-omics data analysis and cross-talk studies in ovarian cancer subtypes.

Stabilized Independent Component Analysis outperforms other methods in finding reproducible signals in tumoral transcriptomes

The paper introduces a stabilized ICA method that improves reproducibility and interpretability of transcriptomic signals across cancer datasets, and applies it to colorectal cancer.

Identification of microRNA clusters cooperatively acting on Epithelial to Mesenchymal Transition in Triple Negative Breast Cancer

The paper identifies miRNA clusters driving triple negative breast cancer subtypes using a new computational pipeline and validates their role in promoting the phenotype through proliferation and EMT control.

Effective normalization for copy number variation in Hi-C data

This paper introduces a normalization method for Hi-C data that accounts for copy number variations, improving downstream analysis of genome organization.

PhysiBoSS: a multi-scale agent based modelling framework integrating physical dimension and cell signalling

PhysiBoSS integrates multi-scale agent-based modelling with Boolean networks to study cell population dynamics and treatment responses in 2D and 3D environments.

Application of Atlas of Cancer Signalling Network in pre-clinical studies

The paper discusses the application of the Atlas of Cancer Signalling Network and NaviCell tools for pre-clinical cancer studies, focusing on drug synergy and intervention design using omics data and signalling maps.

Moonlight: a tool for biological interpretation and driver genes discovery

Moonlight identifies cancer driver genes and their roles by integrating functional analysis and gene regulatory networks from expression data, distinguishing tumor suppressors, oncogenes, and moonlight genes.

Signalling maps in cancer research: construction and data analysis

The paper describes the construction of cancer signalling maps and introduces the NaviCell platform for their exploration and analysis.

Classification Of Gene Signatures For Their Information Value And Functional Redundancy

The paper defines a set of informative gene signatures from pan-cancer data and introduces InfoSigMap to visualize their compositional and functional redundancies.

Differential epigenetic landscapes and transcription factors explain X-linked gene behaviours during X-chromosome reactivation in the mouse inner cell mass

The paper investigates X-linked gene reactivation in mouse inner cell mass by analyzing epigenetic marks and transcription factors, finding differential reactivation speeds and mechanisms.

Determining the optimal number of independent components for reproducible transcriptomic data analysis

This paper introduces a method to determine the optimal number of independent components for reproducible transcriptomic data analysis using ICA stability rankings.

NaviCom: A web application to create interactive molecular network portraits using multi-level omics data

NaviCom provides a web platform to visualize multi-level omics data on molecular network maps to aid in understanding disease mechanisms.

DeDaL: Cytoscape 3.0 app for producing and morphing data-driven and structure-driven network layouts

DeDaL creates data-driven and structure-driven network layouts in Cytoscape using dimension reduction and morphing techniques to align with molecular profiling data.