Marouen Ben Guebila

BLOBFISH identifies gene regulatory subnetworks connecting predefined gene sets using breadth-first search on bipartite graphs, applied to GTEx data to explore tissue-specific relationships.

The paper identifies sex-biased regulation of cancer genes in normal tissues, linking these differences to sex-specific cancer incidence patterns.

This study identifies aging-related changes in gene regulatory networks linked to LUAD risk, prognosis, and therapy response using multi-omics data from GTEx and TCGA.

The paper identifies sex differences in lung adenocarcinoma by integrating multi-omics data to discover sex-biased signaling pathways and potential sex-specific therapies.

The paper develops a Nextflow workflow for reproducibly inferring regulatory networks from TCGA data, showing its application to colon cancer subtypes.

This study investigates how loss of the Y chromosome in lung adenocarcinoma affects the tumor immune environment and response to immunotherapy.

BONOBO infers sample-specific co-expression networks to capture individual heterogeneity in gene regulation, outperforming existing methods in yeast, breast cancer, and thyroid tissue analyses.

This paper introduces COBRA, a method to correct batch effects in gene co-expression networks, showing that standard batch correction does not fully eliminate spurious co-expression associations.

SpaCeNet identifies intracellular and intercellular molecular networks from spatial omics data by analyzing conditional independence relations between variables within and across cells.

The paper introduces netZoo, an open-source software suite for analyzing biological networks, demonstrating its use on cancer cell line data to identify drug resistance and cancer development factors.

The paper introduces EGRET, a method to infer genotype-specific gene regulatory networks and identifies regulatory differences associated with diseases in specific cell types.

gpuZoo accelerates gene regulatory network inference by using GPU acceleration, achieving faster and cheaper runtime compared to CPU-based methods.

GRAND is a database of gene regulatory network models across human conditions, enabling comparison between biological states and drug response prediction.

The paper identifies PD1 signaling regulation as a prognostic marker in glioblastoma using gene regulatory network analysis across two cohorts.

The paper introduces VFFVA, a C implementation of Flux Variability Analysis that achieves a threefold speedup and 14-fold memory reduction compared to fastFVA.

This paper presents a theoretical framework for PANDA, a gene regulatory network inference method, and shows it outperforms state-of-the-art methods in predicting transcription factor binding.

The paper develops a whole-body model of T1D integrating metabolic and regulatory processes to predict pathophysiological mechanisms and potential therapeutic targets.