ArticleBMC bioinformatics2020
Network hub-node prioritization of gene regulation with intra-network association.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 20 citations in OpenAlex.
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- Isolation and Identification of Inter-Correlated Genes from the Invasive Sun CoralsInternational journal of molecular sciences · 2025Article
- In-silico analysis predicts disruption of normal angiogenesis as a causative factor in osteoporosis pathogenesis.BMC genomic data · 2024Article
- Understanding the need for digital twins' data in patient advocacy and forecasting oncology.Frontiers in artificial intelligence · 2023Article
- A Bayesian approach to differential edges with probabilistic interactions: applications in association and classification.Bioinformatics advances · 2023Article
- Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs.Life (Basel, Switzerland) · 2022Article
- Surveying lncRNA-lncRNA cooperations reveals dominant effect on tumor immunity cross cancers.Communications biology · 2022Article
- Identifying preeclampsia-associated genes using a control theory method.Briefings in functional genomics · 2022Article
- Probabilistic edge inference of gene networks with markov random field-based bayesian learning.Frontiers in genetics · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundTo identify and prioritize the influential hub genes in a gene-set or biological pathway, most analyses rely on calculation of marginal effects or tests of statistical significance. These procedures may be inappropriate since hub nodes are common connection points and therefore may interact with other nodes more often than non-hub nodes do. Such dependence among gene nodes can be conjectured based on the topology of the pathway network or the correlation between them.
resultsHere we develop a pathway activity score incorporating the marginal (local) effects of gene nodes as well as intra-network affinity measures. This score summarizes the expression levels in a gene-set/pathway for each sample, with weights on local and network information, respectively. The score is next used to examine the impact of each node through a leave-one-out evaluation. To illustrate the procedure, two cancer studies, one involving RNA-Seq from breast cancer patients with high-grade ductal carcinoma in situ and one microarray expression data from ovarian cancer patients, are used to assess the performance of the procedure, and to compare with existing methods, both ones that do and do not take into consideration correlation and network information. The hub nodes identified by the proposed procedure in the two cancer studies are known influential genes; some have been included in standard treatments and some are currently considered in clinical trials for target therapy. The results from simulation studies show that when marginal effects are mild or weak, the proposed procedure can still identify causal nodes, whereas methods relying only on marginal effect size cannot.
conclusionsThe NetworkHub procedure proposed in this research can effectively utilize the network information in combination with local effects derived from marker values, and provide a useful and complementary list of recommendations for prioritizing causal hubs.
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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.