Evidence map›Paper›PMID 31594828›Full record

ArticlemSystems2019

Current State of and Future Opportunities for Prediction in Microbiome Research: Report from the Mid-Atlantic Microbiome Meet-up in Baltimore on 9 January 2019.

Eric Sakowski, Gherman Uritskiy, Rachel Cooper, Maya Gomes, Michael R McLaren, Jacquelyn S Meisel, Rebecca L Mickol, C David Mintz, Emmanuel F Mongodin, Mihai Pop and 20 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in mSystems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Microbial diversity in extreme environments.Nature reviews. Microbiology · 2022
    Review
  5. Predicting microbiomes through a deep latent space.Bioinformatics (Oxford, England) · 2021
    Article
  6. Mathematical-based microbiome analytics for clinical translation.Computational and structural biotechnology journal · 2021
    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

30 authors.

Eric Sakowski *Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
Gherman Uritskiy *Department of Biology, Johns Hopkins University, Baltimore, Maryland, USA.
Rachel CooperMolecular and Comparative Pathobiology, Johns Hopkins University, Baltimore, Maryland, USA.
Maya GomesDepartment of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, Maryland, USA.
Michael R McLarenDepartment of Population Health and Pathobiology, North Carolina State University, Raleigh, North Carolina, USA.
Jacquelyn S MeiselCenter for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA.
Rebecca L MickolAmerican Society for Engineering Education, Washington, DC, USA.
C David MintzDepartment of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Emmanuel F MongodinUniversity of Maryland School of Medicine, Institute for Genome Sciences, Baltimore, Maryland, USA.
Mihai PopCenter for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA.
Mohammad Arifur RahmanDepartment of Computer Science, George Mason University, Fairfax, Virginia, USA.
Alvaro SanchezDepartment of Ecology and Evolutionary Biology, Yale University, New Haven Connecticut, USA.
Winston TimpDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.ORCID https://orcid.org/0000-0003-2083-6027
Jeseth Delgado VelaDepartment of Civil and Environmental Engineering, Howard University, Washington, DC, USA.
Carly Muletz WolzCenter for Conservation Genomics, Smithsonian National Zoological Park & Conservation Biology Institute, Washington, DC, USA.
Joseph P ZackularDepartment of Pathology and Laboratory Medicine, University of Pennsylvania and Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Jessica ChopykSchool of Public Health, University of Maryland, College Park, Maryland, USA.
Seth CommichauxCenter for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA.
Meghan DavisDepartment of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Douglas DluzenDepartment of Biology, Morgan State University, Baltimore, Maryland, USA.
Sukirth M GanesanNational Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, Maryland, USA.
Muyideen HarunaDepartment of Biology, Morgan State University, Baltimore, Maryland, USA.
Dan NaskoCenter for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA.
Mary J ReganUniversity of Maryland School of Nursing, Baltimore, Maryland, USA.
Saul SarriaCenter for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA.
Nidhi ShahCenter for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA.
Brook StacyCenter for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA.
Dylan TaylorCenter for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland, USA.
Jocelyne DiRuggieroDepartment of Biology, Johns Hopkins University, Baltimore, Maryland, USA jdiruggiero@jhu.edu sprehei1@jhu.edu.ORCID https://orcid.org/0000-0001-6721-8061
Sarah P PreheimDepartment of Environmental Health and Engineering, Johns Hopkins University, Baltimore, Maryland, USA jdiruggiero@jhu.edu sprehei1@jhu.edu.ORCID https://orcid.org/0000-0001-7365-2382

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate predictions across multiple fields of microbiome research have far-reaching benefits to society, but there are few widely accepted quantitative tools to make accurate predictions about microbial communities and their functions. More discussion is needed about the current state of microbiome analysis and the tools required to overcome the hurdles preventing development and implementation of predictive analyses. We summarize the ideas generated by participants of the Mid-Atlantic Microbiome Meet-up in January 2019. While it was clear from the presentations that most fields have advanced beyond simple associative and descriptive analyses, most fields lack essential elements needed for the development and application of accurate microbiome predictions. Participants stressed the need for standardization, reproducibility, and accessibility of quantitative tools as key to advancing predictions in microbiome analysis. We highlight hurdles that participants identified and propose directions for future efforts that will advance the use of prediction in microbiome research.

Indexed as

bioinformaticsconceptual modelsmachine learningmetagenomicsmicrobiomepredictionquantitative models

Identifiers

PMID31594828
PMCPMC6787564

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.