ArticleBiology direct2016
Unexpected links reflect the noise in networks.
Article in Biology direct, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Multi-omic network analysis identified betacellulin as a novel target of omega-3 fatty acid attenuation of western diet-induced nonalcoholic steatohepatitis.EMBO molecular medicine · 2023Pooled it
- Intestinal microbiota signatures of clinical response and immune-related adverse events in melanoma patients treated with anti-PD-1.Nature medicine · 2022Pooled it
- Fecal microbiota transplant overcomes resistance to anti-PD-1 therapy in melanoma patients.Science (New York, N.Y.) · 2021Trial
- Host response to cholestyramine can be mediated by the gut microbiota.Microbiome research reports · 2024Article
- Reducing gut microbiome-driven adipose tissue inflammation alleviates metabolic syndrome.Microbiome · 2023Article
- 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
- Microbiota and adipocyte mitochondrial damage in type 2 diabetes are linked by Mmp12+ macrophages.The Journal of experimental medicine · 2022Article
- Microbiota triggers STING-type I IFN-dependent monocyte reprogramming of the tumor microenvironment.Cell · 2021Article
- Transkingdom interactions between Lactobacilli and hepatic mitochondria attenuate western diet-induced diabetes.Nature communications · 2021Article
- Dietary Indole-3-Carbinol Activates AhR in the Gut, Alters Th17-Microbe Interactions, and Exacerbates Insulitis in NOD Mice.Frontiers in immunology · 2020Article
- Improvements in Metabolic Syndrome by Xanthohumol Derivatives Are Linked to Altered Gut Microbiota and Bile Acid Metabolism.Molecular nutrition & food research · 2020Article
- Unprecedented bacterial community richness in soybean nodules vary with cultivar and water status.Microbiome · 2019Article
- CVID enteropathy is characterized by exceeding low mucosal IgA levels and interferon-driven inflammation possibly related to the presence of a pathobiont.Clinical immunology (Orlando, Fla.) · 2018Article
- Distribution Patterns of Microbial Community Structure Along a 7000-Mile Latitudinal Transect from the Mediterranean Sea Across the Atlantic Ocean to the Brazilian Coastal Sea.Microbial ecology · 2018Article
- Transkingdom Networks: A Systems Biology Approach to Identify Causal Members of Host-Microbiota Interactions.Methods in molecular biology (Clifton, N.J.) · 2018Article
- Microbial Community Structure and Associations During a Marine Dinoflagellate Bloom.Frontiers in microbiology · 2018Article
- Antibiotic-Induced Alterations in Gut Microbiota Are Associated with Changes in Glucose Metabolism in Healthy Mice.Frontiers in microbiology · 2017Article
- Akkermansia muciniphila mediates negative effects of IFNγ on glucose metabolism.Nature communications · 2016Article
- Differentially correlated genes in co-expression networks control phenotype transitions.F1000Research · 2016Article
- Reverse enGENEering of Regulatory Networks from Big Data: A Roadmap for Biologists.Bioinformatics and biology insights · 2015Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGene covariation networks are commonly used to study biological processes. The inference of gene covariation networks from observational data can be challenging, especially considering the large number of players involved and the small number of biological replicates available for analysis.
resultsWe propose a new statistical method for estimating the number of erroneous edges in reconstructed networks that strongly enhances commonly used inference approaches. This method is based on a special relationship between sign of correlation (positive/negative) and directionality (up/down) of gene regulation, and allows for the identification and removal of approximately half of all erroneous edges. Using the mathematical model of Bayesian networks and positive correlation inequalities we establish a mathematical foundation for our method. Analyzing existing biological datasets, we find a strong correlation between the results of our method and false discovery rate (FDR). Furthermore, simulation analysis demonstrates that our method provides a more accurate estimate of network error than FDR.
conclusionsThus, our study provides a new robust approach for improving reconstruction of covariation networks. REVIEWERS: This article was reviewed by Eugene Koonin, Sergei Maslov, Daniel Yasumasa Takahashi.
Indexed as
Identifiers
What OpenQuestion holds
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.