ReviewFrontiers in genetics2020
Network Diffusion Promotes the Integrative Analysis of Multiple Omics.
Review in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Article
- Identification of antimicrobial resistance genes in Escherichia coli through network diffusion.The Journal of antimicrobial chemotherapy · 2026Article
- Functional Interpretation of Omics Data: From Pathways to Networks.Advances in experimental medicine and biology · 2026Review
- DeepGraphMut: a graph-based deep learning method for cancer prognosis using somatic mutation profile.Briefings in bioinformatics · 2025Article
- Reconciling multiple connectivity-based systems biology methods for drug repurposing.Briefings in bioinformatics · 2025Review
- Network-based multi-omics integrative analysis methods in drug discovery: a systematic review.BioData mining · 2025Review
- AMEND 2.0: module identification and multi-omic data integration with multiplex-heterogeneous graphs.BMC bioinformatics · 2025Article
- RWRtoolkit: multi-omic network analysis using random walks on multiplex networks in any species.GigaScience · 2025Article
- Article
- Unveiling hidden connections in omics data via pyPARAGON: an integrative hybrid approach for disease network construction.Briefings in bioinformatics · 2024Article
- Network dynamics and therapeutic aspects of mRNA and protein markers with the recurrence sites of pancreatic cancer.Heliyon · 2024Article
- Focal adhesion is associated with lithium response in bipolar disorder: evidence from a network-based multi-omics analysis.Molecular psychiatry · 2024Article
- Curation of causal interactions mediated by genes associated with autism accelerates the understanding of gene-phenotype relationships underlying neurodevelopmental disorders.Molecular psychiatry · 2024Article
- DrDimont: explainable drug response prediction from differential analysis of multi-omics networks.Bioinformatics (Oxford, England) · 2022Article
- Computational approaches for network-based integrative multi-omics analysis.Frontiers in molecular biosciences · 2022Review
- Construction and contextualization approaches for protein-protein interaction networks.Computational and structural biotechnology journal · 2022Review
- Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context.Frontiers in molecular biosciences · 2022Review
- Article
- Characterization and comparison of gene-centered human interactomes.Briefings in bioinformatics · 2021Article
- Identifying anti-TNF response biomarkers in ulcerative colitis using a diffusion-based signalling model.Bioinformatics advances · 2021Article
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4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of integrative methods is one of the main challenges in bioinformatics. Network-based methods for the analysis of multiple gene-centered datasets take into account known and/or inferred relations between genes. In the last decades, the mathematical machinery of network diffusion-also referred to as network propagation-has been exploited in several network-based pipelines, thanks to its ability of amplifying association between genes that lie in network proximity. Indeed, network diffusion provides a quantitative estimation of network proximity between genes associated with one or more different data types, from simple binary vectors to real vectors. Therefore, this powerful data transformation method has also been increasingly used in integrative analyses of multiple collections of biological scores and/or one or more interaction networks. We present an overview of the state of the art of bioinformatics pipelines that use network diffusion processes for the integrative analysis of omics data. We discuss the fundamental ways in which network diffusion is exploited, open issues and potential developments in the field. Current trends suggest that network diffusion is a tool of broad utility in omics data analysis. It is reasonable to think that it will continue to be used and further refined as new data types arise (e.g. single cell datasets) and the identification of system-level patterns will be considered more and more important in omics data analysis.
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