Jacques Colinge

The paper introduces an expanded framework for ligand-receptor network inference that integrates multiple scoring strategies and analyzes single-cell transcriptomics and proteomics data.

This study uses circulating DNA to reveal nucleosome occupancy patterns linked to DNA affinity and shows their utility in cancer detection with high specificity and sensitivity.

This study creates a single-cell atlas of murine ankle synovium across age, identifying age-related changes in fibroblasts and macrophages linked to fibrosis.

This paper develops a method using circulating DNA fragment end motifs and machine learning to detect cancer with high accuracy across multiple cancer types.

This study identifies RIP140 as a regulator of IFNγ signaling in breast cancer, showing its repression of IFNγ-induced GBP1 expression and its role in enhancing IFNγ's anti-tumor effects.

The paper improves Modular Response Analysis by addressing noise sensitivity, enabling non-independent perturbations, and incorporating nonlinearity and prior knowledge.

This paper introduces a Bayesian modeling approach to capture population protein dynamics using cohort data, showing its ability to handle inter-individual variability.

This study develops a 3-compartment model to simultaneously analyze protein dynamics in blood plasma and cerebrospinal fluid, including a hidden CNS compartment, and applies it to 69 proteins from a single individual.

BulkSignalR infers ligand-receptor networks from bulk and spatial transcriptomic data, showing higher quality inferences compared to existing tools.

The paper reformulates modular response analysis as a multilinear regression problem to improve stability and performance in inferring biological networks from perturbation data.

This paper applies and improves modular response analysis to infer gene regulatory networks in large biological systems with over 1,000 components.

The paper investigates how loss of epithelial polarity and Notch signaling interact in Drosophila neoplastic growth, showing specific transcriptional dependencies and novel factors involved.

The paper introduces aiMeRA, an R package for modular response analysis, and applies it to study estrogen and retinoic acid receptor crosstalk using genomic data.

SingleCellSignalR infers intercellular networks from single-cell transcriptomics and identifies a communication structure from external to basal layers in mouse epidermis.

The paper introduces a proteomics framework to map protein turnover in human cerebrospinal fluid, identifying turnover rates for nearly 200 proteins and revealing relationships between turnover and protein origin.