ArticleScientific reports2018
ModuleDiscoverer: Identification of regulatory modules in protein-protein interaction networks.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Article
- Identification of diagnostic microRNAs and their target genes in multiple sclerosis based on interactome networks using an in silico approach.Current journal of neurology · 2025Article
- Hypothesis generation for rare and undiagnosed diseases through clustering and classifying time-versioned biological ontologies.PloS one · 2024Article
- The Extent of Edgetic Perturbations in the Human Interactome Caused by Population-Specific Mutations.Biomolecules · 2023Article
- Structural Optimization Design of Microfluidic Chips Based on Fast Sequence Pair Algorithm.Micromachines · 2023Article
- DM-MOGA: a multi-objective optimization genetic algorithm for identifying disease modules of non-small cell lung cancer.BMC bioinformatics · 2023Article
- Persistent homology analysis of type 2 diabetes genome-wide association studies in protein-protein interaction networks.Frontiers in genetics · 2023Article
- Network-based approaches for modeling disease regulation and progression.Computational and structural biotechnology journal · 2023Review
- NetMix2: A Principled Network Propagation Algorithm for Identifying Altered Subnetworks.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- A statistical physics approach for disease module detection.Genome research · 2022Article
- Review
- Construction of disease-specific cytokine profiles by associating disease genes with immune responses.PLoS computational biology · 2022Article
- Protein interaction networks provide insight into fetal origins of chronic obstructive pulmonary disease.Respiratory research · 2022Article
- Temporal and sequential order of nonoverlapping gene networks unraveled in mated female Drosophila.Life science alliance · 2022Article
- MODalyseR-a novel software for inference of disease module hub regulators identified a putative multiple sclerosis regulator supported by independent eQTL data.Bioinformatics advances · 2022Article
- Article
- Mesenchymal stromal cells mitigate liver damage after extended resection in the pig by modulating thrombospondin-1/TGF-β.NPJ Regenerative medicine · 2021Article
- Drug repositioning by merging active subnetworks validated in cancer and COVID-19.Scientific reports · 2021Article
- Single-cell network biology for resolving cellular heterogeneity in human diseases.Experimental & molecular medicine · 2020Review
- Finding disease modules for cancer and COVID-19 in gene co-expression networks with the Core&Peel method.Scientific reports · 2020Article
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7 authors.
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Abstract
The identification of disease-associated modules based on protein-protein interaction networks (PPINs) and gene expression data has provided new insights into the mechanistic nature of diverse diseases. However, their identification is hampered by the detection of protein communities within large-scale, whole-genome PPINs. A presented successful strategy detects a PPIN's community structure based on the maximal clique enumeration problem (MCE), which is a non-deterministic polynomial time-hard problem. This renders the approach computationally challenging for large PPINs implying the need for new strategies. We present ModuleDiscoverer, a novel approach for the identification of regulatory modules from PPINs and gene expression data. Following the MCE-based approach, ModuleDiscoverer uses a randomization heuristic-based approximation of the community structure. Given a PPIN of Rattus norvegicus and public gene expression data, we identify the regulatory module underlying a rodent model of non-alcoholic steatohepatitis (NASH), a severe form of non-alcoholic fatty liver disease (NAFLD). The module is validated using single-nucleotide polymorphism (SNP) data from independent genome-wide association studies and gene enrichment tests. Based on gene enrichment tests, we find that ModuleDiscoverer performs comparably to three existing module-detecting algorithms. However, only our NASH-module is significantly enriched with genes linked to NAFLD-associated SNPs. ModuleDiscoverer is available at http://www.hki-jena.de/index.php/0/2/490 (Others/ModuleDiscoverer).
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