ArticleBMC bioinformatics2016
A powerful score-based statistical test for group difference in weighted biological networks.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Development trends of etiological research contents and methods of noncommunicable diseases.Health care science · 2023Article
- Network regression analysis in transcriptome-wide association studies.BMC genomics · 2022Article
- Simultaneous differential network analysis and classification for matrix-variate data with application to brain connectivity.Biostatistics (Oxford, England) · 2022Article
- Integrated Multi-Omics Analysis Model to Identify Biomarkers Associated With Prognosis of Breast Cancer.Frontiers in oncology · 2022Article
- Brain connectivity alteration detection via matrix-variate differential network model.Biometrics · 2021Article
- 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
- A new insight into underlying disease mechanism through semi-parametric latent differential network model.BMC bioinformatics · 2018Article
- JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data.Bioinformatics (Oxford, England) · 2017Article
- Rewiring of the inferred protein interactome during blood development studied with the tool PPICompare.BMC systems biology · 2017Article
- A powerful weighted statistic for detecting group differences of directed biological networks.Scientific reports · 2016Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundComplex disease is largely determined by a number of biomolecules interwoven into networks, rather than a single biomolecule. A key but inadequately addressed issue is how to test possible differences of the networks between two groups. Group-level comparison of network properties may shed light on underlying disease mechanisms and benefit the design of drug targets for complex diseases. We therefore proposed a powerful score-based statistic to detect group difference in weighted networks, which simultaneously capture the vertex changes and edge changes.
resultsSimulation studies indicated that the proposed network difference measure (NetDifM) was stable and outperformed other methods existed, under various sample sizes and network topology structure. One application to real data about GWAS of leprosy successfully identified the specific gene interaction network contributing to leprosy. For additional gene expression data of ovarian cancer, two candidate subnetworks, PI3K-AKT and Notch signaling pathways, were considered and identified respectively.
conclusionsThe proposed method, accounting for the vertex changes and edge changes simultaneously, is valid and powerful to capture the group difference of biological networks.
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Registered trials
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.