ArticleFrontiers in genetics2020
HARMONIES: A Hybrid Approach for Microbiome Networks Inference via Exploiting Sparsity.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis 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
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-analysis of microbiome association networks reveal patterns of dysbiosis in diseased microbiomes.Scientific reports · 2022Pooled it
- Evaluating microbial network inference methods: moving beyond synthetic data with reproducibility-driven benchmarks.BMC bioinformatics · 2026Article
- MiCoDe: a web tool for performing microbiome community detection using a Bayesian weighted stochastic block model.Bioinformatics (Oxford, England) · 2025Article
- Recent advancements in artificial intelligence-powered cancer prediction from oral microbiome.Periodontology 2000 · 2025Review
- Cross-validation for training and testing co-occurrence network inference algorithms.BMC bioinformatics · 2025Article
- A Generalized Bayesian Stochastic Block Model for Microbiome Community Detection.Statistics in medicine · 2025Article
- UniCor and UniCorP: a novel metric and hierarchical feature selection algorithm for microbial community analysis.ISME communications · 2025Article
- SpeSpeNet: an interactive and user-friendly tool to create and explore microbial correlation networks.ISME communications · 2025Article
- OneNet-One network to rule them all: Consensus network inference from microbiome data.PLoS computational biology · 2024Article
- Networks as tools for defining emergent properties of microbiomes and their stability.Microbiome · 2024Review
- A review of machine learning methods for cancer characterization from microbiome data.NPJ precision oncology · 2024Review
- Analysis of Microbiome Data.Annual review of statistics and its application · 2024Article
- Progress on network modeling and analysis of gut microecology: a review.Applied and environmental microbiology · 2024Review
- Gut-brain axis and neurodegeneration: mechanisms and therapeutic potentials.Frontiers in neuroscience · 2024Review
- MicroNet-MIMRF: a microbial network inference approach based on mutual information and Markov random fields.Bioinformatics advances · 2024Article
- Inferring microbial co-occurrence networks from amplicon data: a systematic evaluation.mSystems · 2023Article
- Connect the dots: sketching out microbiome interactions through networking approaches.Microbiome research reports · 2023Review
- Microbiota DNA isolation, 16S rRNA amplicon sequencing, and bioinformatic analysis for bacterial microbiome profiling of rodent fecal samples.STAR protocols · 2022Article
- mbDenoise: microbiome data denoising using zero-inflated probabilistic principal components analysis.Genome biology · 2022Article
- A Survey of Statistical Methods for Microbiome Data Analysis.Frontiers in applied mathematics and statistics · 2022Article
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
Abstract
The human microbiome is a collection of microorganisms. They form complex communities and collectively affect host health. Recently, the advances in next-generation sequencing technology enable the high-throughput profiling of the human microbiome. This calls for a statistical model to construct microbial networks from the microbiome sequencing count data. As microbiome count data are high-dimensional and suffer from uneven sampling depth, over-dispersion, and zero-inflation, these characteristics can bias the network estimation and require specialized analytical tools. Here we propose a general framework, HARMONIES, Hybrid Approach foR MicrobiOme Network Inferences via Exploiting Sparsity, to infer a sparse microbiome network. HARMONIES first utilizes a zero-inflated negative binomial (ZINB) distribution to model the skewness and excess zeros in the microbiome data, as well as incorporates a stochastic process prior for sample-wise normalization. This approach infers a sparse and stable network by imposing non-trivial regularizations based on the Gaussian graphical model. In comprehensive simulation studies, HARMONIES outperformed four other commonly used methods. When using published microbiome data from a colorectal cancer study, it discovered a novel community with disease-enriched bacteria. In summary, HARMONIES is a novel and useful statistical framework for microbiome network inference, and it is available at https://github.com/shuangj00/HARMONIES.
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.