ArticleF1000Research2016
Differentially correlated genes in co-expression networks control phenotype transitions.
Article in F1000Research, 2016. 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, 11 citations in OpenAlex.
- Transkingdom Network Analysis (TkNA): a systems framework for inferring causal factors underlying host-microbiota and other multi-omic interactions.Nature protocols · 2024Review
- Host response to cholestyramine can be mediated by the gut microbiota.Microbiome research reports · 2024Article
- Transkingdom Network Analysis (TkNA): a systems framework for inferring causal factors underlying host-microbiota and other multi-omic interactions.bioRxiv : the preprint server for biology · 2023Article
- Using transcriptomics to predict and visualize disease status in bighorn sheep (Conservation physiology · 2022Article
- An eco-evo-devo genetic network model of stress response.Horticulture research · 2022Article
- Review
- Transkingdom network reveals bacterial players associated with cervical cancer gene expression program.PeerJ · 2018Article
- Transkingdom Networks: A Systems Biology Approach to Identify Causal Members of Host-Microbiota Interactions.Methods in molecular biology (Clifton, N.J.) · 2018Article
- Differentially correlated genes in co-expression networks control phenotype transitions.F1000Research · 2016Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCo-expression networks are a tool widely used for analysis of "Big Data" in biology that can range from transcriptomes to proteomes, metabolomes and more recently even microbiomes. Several methods were proposed to answer biological questions interrogating these networks. Differential co-expression analysis is a recent approach that measures how gene interactions change when a biological system transitions from one state to another. Although the importance of differentially co-expressed genes to identify dysregulated pathways has been noted, their role in gene regulation is not well studied. Herein we investigated differentially co-expressed genes in a relatively simple mono-causal process (B lymphocyte deficiency) and in a complex multi-causal system (cervical cancer).
methodsCo-expression networks of B cell deficiency (Control and BcKO) were reconstructed using Pearson correlation coefficient for two
conclusionIdentifying differentially co-expressed genes in co-expression networks is an important tool in detecting regulatory genes involved in alterations of phenotype.
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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.