Evidence map›Paper›PMID 40266486›Full record

ArticleBrazilian journal of microbiology : [publication of the Brazilian Society for Microbiology]2025

Pato: prediction of probiotic bacteria using metabolic features.

Rafaella Sinnott Dias, Daniela Peres Martinez, Fábio Pereira Leivas Leite, Luciana Farias da Costa de Avila, Frederico Schmitt Kremer

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology], 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. International journal of molecular sciences · 2026
    Article
  2. PROBEAT: PRObiotic Bacterial gEnome Analysis Toolkit.Current issues in molecular biology · 2026
    Article
  3. 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

5 authors.

Rafaella Sinnott DiasLaboratório de Bioinformática OmixLab, Centro de Desenvolvimento Tecnológico, Universidade Federal de Pelotas, Pelotas, Rio Grande do Sul, Brazil.
Daniela Peres MartinezLaboratório de Bioinformática OmixLab, Centro de Desenvolvimento Tecnológico, Universidade Federal de Pelotas, Pelotas, Rio Grande do Sul, Brazil.
Fábio Pereira Leivas LeiteLaboratório de Bioinformática OmixLab, Centro de Desenvolvimento Tecnológico, Universidade Federal de Pelotas, Pelotas, Rio Grande do Sul, Brazil.
Luciana Farias da Costa de AvilaLaboratório de Parasitologia, Faculdade de Medicina, Universidade Federal do Rio Grande, Rio Grande, Rio Grande do Sul, Brazil.
Frederico Schmitt KremerLaboratório de Bioinformática OmixLab, Centro de Desenvolvimento Tecnológico, Universidade Federal de Pelotas, Pelotas, Rio Grande do Sul, Brazil. fred.s.kremer@gmail.com.ORCID http://orcid.org/0000-0001-7284-6054

Funding

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
6 · The paper itself

Abstract

Probiotics have gained recognition for their health-promoting benefits, particularly in the gastrointestinal and immunological systems. Among promising probiotic candidates, Lactobacillus strains, belonging to the lactic acid bacteria (LAB) group, play a significant role in human microbiota. To aid in the in silico identification of Lactobacillus strains with probiotic potential, this study presents a novel classification approach based on functional and metabolism-related elements, which offers improved accuracy and explainability compared to traditional k-mer-based methods. By considering the functional characteristics of genomic sequences, this approach contributes to a clearer understanding of the traits associated with probiotic activity, facilitating the selection of strains with optimal health-promoting attributes. The webserver is available at http://200.132.101.156:5001/ .

Indexed as

Computational BiologyLactobacillusProbioticsGastrointestinal MicrobiomeGenome, BacterialHumansGenome annotationLactobacillusMachine learningProbiotics

Identifiers

PMID40266486
PMCPMC12095092

What Socratic holds

Textmetadata
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 Socratic graph.