SynthesisPLoS computational biology2009
Meta-analysis of inter-species liver co-expression networks elucidates traits associated with common human diseases.
Synthesis in PLoS computational biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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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
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 59 citations in OpenAlex.
- Interaction between allelic variations in vitamin D receptor and retinoid X receptor genes on metabolic traits.BMC genetics · 2014Pooled it
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- Network-based protein-protein interaction prediction method maps perturbations of cancer interactome.PLoS genetics · 2021Article
- Myeloid Cell-associated Resistance to PD-1/PD-L1 Blockade in Urothelial Cancer Revealed Through Bulk and Single-cell RNA Sequencing.Clinical cancer research : an official journal of the American Association for Cancer Research · 2021Article
- Comparative Analyses of Gene Co-expression Networks: Implementations and Applications in the Study of Evolution.Frontiers in genetics · 2021Review
- Comparative functional genomic analysis of Alzheimer's affected and naturally aging brains.PeerJ · 2020Article
- Using Network Pharmacology to Explore Potential Treatment Mechanism for Coronary Heart Disease Using Chuanxiong and Jiangxiang Essential Oils in Jingzhi Guanxin Prescriptions.Evidence-based complementary and alternative medicine : eCAM · 2019Article
- Study of Meta-analysis strategies for network inference using information-theoretic approaches.BioData mining · 2017Article
- A comparative evaluation of data-merging and meta-analysis methods for reconstructing gene-gene interactions.BMC bioinformatics · 2016Article
- Identifying Network Perturbation in Cancer.PLoS computational biology · 2016Article
- Article
- GeneFriends: a human RNA-seq-based gene and transcript co-expression database.Nucleic acids research · 2015Article
- Co-expression network analysis identifies transcriptional modules in the mouse liver.Molecular genetics and genomics : MGG · 2014Article
- Common dysregulation network in the human prefrontal cortex underlies two neurodegenerative diseases.Molecular systems biology · 2014Article
- Network-based differential gene expression analysis suggests cell cycle related genes regulated by E2F1 underlie the molecular difference between smoker and non-smoker lung adenocarcinoma.BMC bioinformatics · 2013Article
- Personalized management of chronic obstructive pulmonary disease via transcriptomic profiling of the airway and lung.Annals of the American Thoracic Society · 2013Review
- Systems Biology Approaches and Applications in Obesity, Diabetes, and Cardiovascular Diseases.Current cardiovascular risk reports · 2013Article
- Rewiring of human lung cell lineage and mitotic networks in lung adenocarcinomas.Nature communications · 2013Article
- Comprehensive literature review and statistical considerations for microarray meta-analysis.Nucleic acids research · 2012Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Co-expression networks are routinely used to study human diseases like obesity and diabetes. Systematic comparison of these networks between species has the potential to elucidate common mechanisms that are conserved between human and rodent species, as well as those that are species-specific characterizing evolutionary plasticity. We developed a semi-parametric meta-analysis approach for combining gene-gene co-expression relationships across expression profile datasets from multiple species. The simulation results showed that the semi-parametric method is robust against noise. When applied to human, mouse, and rat liver co-expression networks, our method out-performed existing methods in identifying gene pairs with coherent biological functions. We identified a network conserved across species that highlighted cell-cell signaling, cell-adhesion and sterol biosynthesis as main biological processes represented in genome-wide association study candidate gene sets for blood lipid levels. We further developed a heterogeneity statistic to test for network differences among multiple datasets, and demonstrated that genes with species-specific interactions tend to be under positive selection throughout evolution. Finally, we identified a human-specific sub-network regulated by RXRG, which has been validated to play a different role in hyperlipidemia and Type 2 diabetes between human and mouse. Taken together, our approach represents a novel step forward in integrating gene co-expression networks from multiple large scale datasets to leverage not only common information but also differences that are dataset-specific.
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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.