Evidence map›Paper›PMID 40591820›Full record

ArticleBioinformatics (Oxford, England)2025

MiCoDe: a web tool for performing microbiome community detection using a Bayesian weighted stochastic block model.

Kevin C Lutz, Shengjie Yang, Tejasv Bedi, Michael L Neugent, Nikita Madhavaram, Bo Yao, Xiaowei Zhan, Nicole J De Nisco, Qiwei Li

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Kevin C LutzDepartment of Health Data Science and Biostatistics, O'Donnell School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX 75390, United States.ORCID 0000-0002-6687-0637
Shengjie YangDepartment of Health Data Science and Biostatistics, O'Donnell School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX 75390, United States.ORCID 0000-0002-2629-9130
Tejasv BediDepartment of Mathematical Sciences, The University of Texas at Dallas, Richardson, TX 75080, United States.ORCID 0000-0001-7532-4075
Michael L NeugentDepartment of Biological Sciences, The University of Texas at Dallas, Richardson, TX 75080, United States.ORCID 0000-0002-9863-9595
Nikita MadhavaramDepartment of Health Data Science and Biostatistics, O'Donnell School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX 75390, United States.ORCID 0009-0006-7923-4783
Bo YaoDepartment of Health Data Science and Biostatistics, O'Donnell School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX 75390, United States.ORCID 0009-0002-2225-9956
Xiaowei ZhanDepartment of Health Data Science and Biostatistics, O'Donnell School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX 75390, United States.ORCID 0000-0002-6249-7193
Nicole J De NiscoDepartment of Biological Sciences, The University of Texas at Dallas, Richardson, TX 75080, United States.ORCID 0000-0002-7670-5301
Qiwei LiDepartment of Mathematical Sciences, The University of Texas at Dallas, Richardson, TX 75080, United States.ORCID 0000-0002-1020-3050

Funding

National Science Foundation 1636933
6 · The paper itself

Abstract

summaryThe Microbiome Community Detector (MiCoDe) software is a free user-friendly web tool that is designed to cluster a network of microbial taxa into communities using a Bayesian weighted stochastic block model. MiCoDe also filters the data automatically and accounts for the challenges of microbiome high-throughput sequencing data including high-dimensionality, compositionality, zero inflation, and nonlinearity. While MiCoDe is based on a rigorous statistical unsupervised learning model, our web tool can be easily used by any investigator. Users simply upload a csv file that contains their taxonomic abundance data where rows correspond to samples and columns correspond to taxa. Then, users make a few selections regarding data transformation, network estimation, and the number of communities in order to run the online analysis. If users are unsure of what selections to make, then they can opt for the default settings as these are our recommended settings. In this paper, we discuss the motivation, methodology, implementation, and results of MiCoDe. We also discuss how MiCoDe can be adapted by the user and how it may evolve over time. Our software is a valuable tool for microbiome community detection. AVAILABILITY AND IMPLEMENTATION: MiCoDe is freely available online at https://lce.biohpc.swmed.edu/micode/, does not require installation, and is not browser-specific. Users can also work locally using our R code, which is freely available on GitHub at https://github.com/klutz920/MiCoDe.

Indexed as

MicrobiotaSoftwareBayes TheoremHigh-Throughput Nucleotide SequencingInternetStochastic Processes

Identifiers

PMID40591820
PMCPMC12263104

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.