Aurélien J. F. Dugourd

Turning Domain Expertise into Multi-Dimensional Evaluation of Biomedical AI with Karenina

This paper introduces Karenina, a framework that evaluates biomedical AI using expert knowledge across multiple dimensions beyond traditional scoring.

Function-driven geometry directs human pilosebaceous unit development

The paper builds a spatiotemporal map of human pilosebaceous unit development using single-cell multiomics and machine learning, showing how epithelial-mesenchymal interactions drive organogenesis and how sebaceous gland programs are disrupted in tumors.

Combining prior knowledge and transcriptomics data for logic models of patient subgroups

The paper introduces FICUS, a framework that integrates omics data with logic models to identify shared signaling mechanisms across patient subgroups.

OmniPath Metabo: chemical structures, interactions and mechanisms to study the metabolome

OmniPath Metabo integrates metabolomics data with prior knowledge to enable mechanistic analysis of the metabolome through harmonized databases and network construction.

OmniPath: integrated knowledgebase for multi-omics analysis

OmniPath integrates molecular knowledge from 168 resources to support multi-omics analysis, offering curated data, predictions, and tools for causal modeling across bulk, single-cell, and spatial data.

Bioinformatic inference of the exercise-responsive control of p70 S6 kinase through RPS6KB1 expression

This study uses a bioinformatic network inference algorithm to identify causal signaling networks regulating RPS6KB1 expression in response to aerobic and resistance exercise in human skeletal muscle.

Comprehensive evaluation of phosphoproteomic-based kinase activity inference

This paper evaluates kinase activity inference methods using phosphoproteomics data and benchmarking approaches, finding that curated kinase-substrate libraries significantly impact results.

A knowledge graph and topological data analysis framework to disentangle the tomato-multi pathogens complex gene regulatory network

The paper constructs a knowledge graph and uses topological data analysis to uncover the gene regulatory network of tomato's response to multiple pathogens, identifying key transcription factors and regulatory hubs.

Modeling causal signal propagation in multi-omic factor space with COSMOS

The paper introduces COSMOS+ to integrate mechanistic prior knowledge with multi-omics data analysis to generate mechanistic hypotheses about disease resistance mechanisms.

MetalinksDB: a flexible and contextualizable resource of metabolite-protein interactions

MetalinksDB provides a comprehensive database of metabolite-protein interactions, enabling context-specific analysis of metabolite-mediated cell-cell communication.

LIANA+: an all-in-one cell-cell communication framework

LIANA+ is a framework for analyzing cell-cell communication in single-cell and spatial transcriptomics data, integrating multiple methods and supporting multi-omics 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.

Comparison of Resources and Methods to infer Cell-Cell Communication from Single-cell RNA Data

The paper compares 15 resources and 6 methods for inferring cell-cell communication from single-cell RNA data, finding significant variation in predicted interactions and biological interpretations.

decoupleR: Ensemble of computational methods to infer biological activities from omics data

decoupleR evaluates methods for inferring biological activities from omics data, finding simple linear models and consensus scores perform best in perturbation experiments.

Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses

COSMOS integrates multi-omics data with prior knowledge to generate mechanistic hypotheses about biological processes in renal cell carcinoma.

Converting networks to predictive logic models from perturbation signalling data with CellNOpt

The paper introduces updates to CellNOpt, a tool for building logic models from perturbation data and prior knowledge networks, including new methods and an interactive interface.

Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer

The paper uses a kinetic model of central carbon metabolism to predict metabolic reprogramming in liver cancer and validates findings through experiments.