ArticleBMC bioinformatics2015
Differential analysis of biological networks.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 41 citations in OpenAlex.
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- Comparing Statistical Tests for Differential Network Analysis of Gene Modules.Frontiers in genetics · 2021Article
- Bayesian differential analysis of gene regulatory networks exploiting genetic perturbations.BMC bioinformatics · 2020Article
- PMINR: Pointwise Mutual Information-Based Network Regression - With Application to Studies of Lung Cancer and Alzheimer's Disease.Frontiers in genetics · 2020Article
- A Machine Learning Method for Identifying Critical Interactions Between Gene Pairs in Alzheimer's Disease Prediction.Frontiers in neurology · 2019Article
- Epigenetics, heritability and longitudinal analysis.BMC genetics · 2018Article
- Network analysis of drug effect on triglyceride-associated DNA methylation.BMC proceedings · 2018Article
- Utilizing networks for differential analysis of chromatin interactions.Journal of bioinformatics and computational biology · 2017Article
- JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data.Bioinformatics (Oxford, England) · 2017Article
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- Rewiring of the inferred protein interactome during blood development studied with the tool PPICompare.BMC systems biology · 2017Article
- Detection of statistically significant network changes in complex biological networks.BMC systems biology · 2017Article
- Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn cancer research, the comparison of gene expression or DNA methylation networks inferred from healthy controls and patients can lead to the discovery of biological pathways associated to the disease. As a cancer progresses, its signalling and control networks are subject to some degree of localised re-wiring. Being able to detect disrupted interaction patterns induced by the presence or progression of the disease can lead to the discovery of novel molecular diagnostic and prognostic signatures. Currently there is a lack of scalable statistical procedures for two-network comparisons aimed at detecting localised topological differences.
resultsWe propose the dGHD algorithm, a methodology for detecting differential interaction patterns in two-network comparisons. The algorithm relies on a statistic, the Generalised Hamming Distance (GHD), for assessing the degree of topological difference between networks and evaluating its statistical significance. dGHD builds on a non-parametric permutation testing framework but achieves computationally efficiency through an asymptotic normal approximation.
conclusionsWe show that the GHD is able to detect more subtle topological differences compared to a standard Hamming distance between networks. This results in the dGHD algorithm achieving high performance in simulation studies as measured by sensitivity and specificity. An application to the problem of detecting differential DNA co-methylation subnetworks associated to ovarian cancer demonstrates the potential benefits of the proposed methodology for discovering network-derived biomarkers associated with a trait of interest.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.