Evidence map›Paper›PMID 33785070›Full record

ArticleGenome biology2021

Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox.

Jakob Wirbel, Konrad Zych, Morgan Essex, Nicolai Karcher, Ece Kartal, Guillem Salazar, Peer Bork, Shinichi Sunagawa, Georg Zeller

Abstract read
In one paragraph

Article in Genome biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 165 papers, 13 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
165citing papers in PubMed, 13 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

165 citing papers in PubMed, 13 syntheses or guidelines pooled it.

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105 more citing papers are in PubMed but not listed here.

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.

Jakob WirbelStructural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), 69117, Heidelberg, Germany.ORCID 0000-0002-4073-3562
Konrad ZychStructural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), 69117, Heidelberg, Germany.ORCID 0000-0001-7426-0516
Morgan EssexStructural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), 69117, Heidelberg, Germany.ORCID 0000-0001-8758-7497
Nicolai KarcherStructural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), 69117, Heidelberg, Germany.ORCID 0000-0001-7894-8182
Ece KartalStructural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), 69117, Heidelberg, Germany.ORCID 0000-0002-7720-455X
Guillem SalazarDepartment of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich, 8093, Zürich, Switzerland.ORCID 0000-0002-9786-1493
Peer BorkStructural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), 69117, Heidelberg, Germany.ORCID 0000-0002-2627-833X
Shinichi SunagawaDepartment of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich, 8093, Zürich, Switzerland.ORCID 0000-0003-3065-0314
Georg ZellerStructural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), 69117, Heidelberg, Germany. zeller@embl.de.ORCID 0000-0003-1429-7485

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers using machine learning (ML). However, metagenomics-specific software is scarce, and overoptimistic evaluation and limited cross-study generalization are prevailing issues. To address these, we developed SIAMCAT, a versatile R toolbox for ML-based comparative metagenomics. We demonstrate its capabilities in a meta-analysis of fecal metagenomic studies (10,803 samples). When naively transferred across studies, ML models lost accuracy and disease specificity, which could however be resolved by a novel training set augmentation strategy. This reveals some biomarkers to be disease-specific, with others shared across multiple conditions. SIAMCAT is freely available from siamcat.embl.de .

Indexed as

Machine LearningMetagenomeMicrobiotaSoftwareComputational BiologyConfounding Factors, EpidemiologicCrohn DiseaseDatabases, GeneticGastrointestinal MicrobiomeHumansMeta-Analysis as TopicMetagenomicsModels, StatisticalROC CurveWorkflowMachine learningMeta-analysisMicrobiome data analysisMicrobiome-wide association studies (MWAS)Statistical modeling

Identifiers

PMID33785070
PMCPMC8008609

What OpenQuestion holds

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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.