ArticleBioinformatics (Oxford, England)2020
MDiNE: a model to estimate differential co-occurrence networks in microbiome studies.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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
27 citing papers in PubMed, 59 citations in OpenAlex.
- Proportionality-based association metrics in count compositional data.NAR genomics and bioinformatics · 2026Article
- Differential analysis of microbial interaction networks.Briefings in bioinformatics · 2026Article
- Decoding immunotherapy response through computational modeling.Nature communications · 2026Review
- Distinct diet-microbiome associations in autism spectrum disorder.Nature communications · 2025Article
- MNetClass: a control-free microbial network clustering framework for identifying central subcommunities across ecological niches.mSystems · 2025Article
- A systematic benchmark of integrative strategies for microbiome-metabolome data.Communications biology · 2025Article
- Cross-validation for training and testing co-occurrence network inference algorithms.BMC bioinformatics · 2025Article
- From variability to stability: Sensitivity of network properties in IBD human gut microbiome studies.Computational and structural biotechnology journal · 2025Article
- Discovery of sparse, reliable omic biomarkers with Stabl.Nature biotechnology · 2024Article
- Differential network connectivity analysis for microbiome data adjusted for clinical covariates using jackknife pseudo-values.BMC bioinformatics · 2024Article
- Alterations in fecal virome and bacteriome virome interplay in children with autism spectrum disorder.Cell reports. Medicine · 2024Article
- SOHPIE: statistical approach via pseudo-value information and estimation for differential network analysis of microbiome data.Bioinformatics (Oxford, England) · 2024Article
- Covariance regression with random forests.BMC bioinformatics · 2023Article
- PLSDA-batch: a multivariate framework to correct for batch effects in microbiome data.Briefings in bioinformatics · 2023Article
- Connect the dots: sketching out microbiome interactions through networking approaches.Microbiome research reports · 2023Review
- C3NA: correlation and consensus-based cross-taxonomy network analysis for compositional microbial data.BMC bioinformatics · 2022Article
- Interpretation of network-based integration from multi-omics longitudinal data.Nucleic acids research · 2022Article
- Impact of Geographical Location on the Gut Microbiota Profile in Egyptian Children with Type 1 Diabetes Mellitus: A Pilot Study.International journal of general medicine · 2022Article
- Modeling Microbial Community Networks: Methods and Tools.Current genomics · 2021Review
- Gut microbiota and short-chain fatty acid alterations in cachectic cancer patients.Journal of cachexia, sarcopenia and muscle · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 1 country.
Funding
Abstract
motivationThe human microbiota is the collection of microorganisms colonizing the human body, and plays an integral part in human health. A growing trend in microbiome analysis is to construct a network to estimate the co-occurrence patterns among taxa through precision matrices. Existing methods do not facilitate investigation into how these networks change with respect to covariates.
resultsWe propose a new model called Microbiome Differential Network Estimation (MDiNE) to estimate network changes with respect to a binary covariate. The counts of individual taxa in the samples are modeled through a multinomial distribution whose probabilities depend on a latent Gaussian random variable. A sparse precision matrix over all the latent terms determines the co-occurrence network among taxa. The model fit is obtained and evaluated using Hamiltonian Monte Carlo methods. The performance of our model is evaluated through an extensive simulation study and is shown to outperform existing methods in terms of estimation of network parameters. We also demonstrate an application of the model to estimate changes in the intestinal microbial network topology with respect to Crohn's disease. AVAILABILITY AND IMPLEMENTATION: MDiNE is implemented in a freely available R package: https://github.com/kevinmcgregor/mdine. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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.