ArticleBMC genomics2011
Identification of dysfunctional modules and disease genes in congenital heart disease by a network-based approach.
Article in BMC genomics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 58 citations in OpenAlex.
- Article
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- Hypothesis generation for rare and undiagnosed diseases through clustering and classifying time-versioned biological ontologies.PloS one · 2024Article
- Bioinformatics and System Biological Approaches for the Identification of Genetic Risk Factors in the Progression of Cardiovascular Disease.Cardiovascular therapeutics · 2022Article
- Dissecting Prognosis Modules and Biomarkers in Glioblastoma Based on Weighted Gene Co-Expression Network Analysis.Cancer management and research · 2021Article
- Graph- and rule-based learning algorithms: a comprehensive review of their applications for cancer type classification and prognosis using genomic data.Briefings in bioinformatics · 2020Review
- Integrated Analysis to Evaluate the Prognostic Value of Signature mRNAs in Glioblastoma Multiforme.Frontiers in genetics · 2020Article
- Identification of glioblastoma gene prognosis modules based on weighted gene co-expression network analysis.BMC medical genomics · 2018Article
- Extracting proteins involved in disease progression using temporally connected networks.BMC systems biology · 2018Article
- Comparative network stratification analysis for identifying functional interpretable network biomarkers.BMC bioinformatics · 2017Article
- Network-Based Approach to Identify Potential Targets and Drugs that Promote Neuroprotection and Neurorepair in Acute Ischemic Stroke.Scientific reports · 2017Article
- Integrated network analysis reveals potentially novel molecular mechanisms and therapeutic targets of refractory epilepsies.PloS one · 2017Article
- Network-based machine learning and graph theory algorithms for precision oncology.NPJ precision oncology · 2017Review
- Quantitative assessment of gene expression network module-validation methods.Scientific reports · 2015Article
- Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data.Current genomics · 2015Article
- Pathway mapping and development of disease-specific biomarkers: protein-based network biomarkers.Journal of cellular and molecular medicine · 2015Review
- Congenital heart disease: the crossroads of genetics, epigenetics and environment.Current genomics · 2014Article
- Survey of network-based approaches to research of cardiovascular diseases.BioMed research international · 2014Review
- Article
- Genetics of congenital heart disease: the glass half empty.Circulation research · 2013Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe incidence of congenital heart disease (CHD) is continuously increasing among infants born alive nowadays, making it one of the leading causes of infant morbidity worldwide. Various studies suggest that both genetic and environmental factors lead to CHD, and therefore identifying its candidate genes and disease-markers has been one of the central topics in CHD research. By using the high-throughput genomic data of CHD which are available recently, network-based methods provide powerful alternatives of systematic analysis of complex diseases and identification of dysfunctional modules and candidate disease genes.
resultsIn this paper, by modeling the information flow from source disease genes to targets of differentially expressed genes via a context-specific protein-protein interaction network, we extracted dysfunctional modules which were then validated by various types of measurements and independent datasets. Network topology analysis of these modules revealed major and auxiliary pathways and cellular processes in CHD, demonstrating the biological usefulness of the identified modules. We also prioritized a list of candidate CHD genes from these modules using a guilt-by-association approach, which are well supported by various kinds of literature and experimental evidence.
conclusionsWe provided a network-based analysis to detect dysfunctional modules and disease genes of CHD by modeling the information transmission from source disease genes to targets of differentially expressed genes. Our method resulted in 12 modules from the constructed CHD subnetwork. We further identified and prioritized candidate disease genes of CHD from these dysfunctional modules. In conclusion, module analysis not only revealed several important findings with regard to the underlying molecular mechanisms of CHD, but also suggested the distinct network properties of causal disease genes which lead to identification of candidate CHD genes.
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