Evidence map›Paper›PMID 36009575›Full record

ArticleBiomedicines2022

Machine Learning Data Analysis Highlights the Role of

Daniele Pietrucci, Adelaide Teofani, Marco Milanesi, Bruno Fosso, Lorenza Putignani, Francesco Messina, Graziano Pesole, Alessandro Desideri, Giovanni Chillemi

Open access · goldAbstract read
In one paragraph

Article in Biomedicines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
4.2field-weighted citation impact, top 5% of its field
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

18 citing papers in PubMed, 48 citations in OpenAlex.

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  6. Distinct gut microbiota profiles in acute vs. chronic sinusitis: potential biomarkers.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025
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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 at 6 institutions in 1 country.

Daniele PietrucciDepartment for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, 01100 Viterbo, Italy.ORCID 0000-0002-4233-0839
Adelaide TeofaniDepartment of Biology, University of Rome Tor Vergata, Via Montpellier 1, 00133 Rome, Italy.
Marco MilanesiDepartment for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, 01100 Viterbo, Italy.ORCID 0000-0001-6244-7455
Bruno FossoDepartment of Biosciences, Biotechnology and Biopharmaceutics, University of Bari "A. Moro", Piazza Umberto I, 1, 70121 Bari, Italy.ORCID 0000-0003-2324-086X
Lorenza PutignaniUnit of Microbiology and Diagnostic Immunology, Units of Microbiomics, Department of Diagnostic and Laboratory Medicine, Bambino Gesù Children's Hospital, IRCCS, 00146 Rome, Italy.ORCID 0000-0003-0134-2830
Francesco MessinaLaboratory of Microbiology and Biological Bank National Institute for Infectious Diseases "Lazzaro Spallanzani" Istituto di Ricovero e Cura a Carattere Scientifico, 00149 Rome, Italy.ORCID 0000-0001-8076-7217
Graziano PesoleInstitute of Biomembranes, Bioenergetics and Molecular Biotechnologies, IBIOM, CNR, 70126 Bari, Italy.ORCID 0000-0003-3663-0859
Alessandro DesideriDepartment of Biology, University of Rome Tor Vergata, Via Montpellier 1, 00133 Rome, Italy.ORCID 0000-0003-1541-4217
Giovanni ChillemiDepartment for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, 01100 Viterbo, Italy.ORCID 0000-0003-3901-6926
Università degli Studi della Tuscia · ITUniversity of Rome Tor Vergata · ITBambino Gesù Children's Hospital · ITInstitute of Biomembranes, Bioenergetics and Molecular Biotechnologies · ITIstituto Nazionale per le Malattie Infettive Lazzaro Spallanzani · ITUniversity of Bari Aldo Moro · IT

Funding

Ministero della Salute RF-2018-12366931Regione Lazio "ProgettiStrategici 2019" Project N.A0320-2019-28090 "HGP-T21"
6 · The paper itself

Abstract

In recent years, the involvement of the gut microbiota in disease and health has been investigated by sequencing the 16S gene from fecal samples. Dysbiotic gut microbiota was also observed in Autism Spectrum Disorder (ASD), a neurodevelopmental disorder characterized by gastrointestinal symptoms. However, despite the relevant number of studies, it is still difficult to identify a typical dysbiotic profile in ASD patients. The discrepancies among these studies are due to technical factors (i.e., experimental procedures) and external parameters (i.e., dietary habits). In this paper, we collected 959 samples from eight available projects (540 ASD and 419 Healthy Controls, HC) and reduced the observed bias among studies. Then, we applied a Machine Learning (ML) approach to create a predictor able to discriminate between ASD and HC. We tested and optimized three algorithms: Random Forest, Support Vector Machine and Gradient Boosting Machine. All three algorithms confirmed the importance of five different genera, including

Indexed as

Alloprevorellaautism spectrum disorderdysbiosisgut microbiotamachine learning data analysisParasutterellatargeted metagenomics

Identifiers

PMID36009575
PMCPMC9405825
OpenAlexW4292547531

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.