Evidence map›Paper›PMID 39655221›Full record

ArticleFrontiers in genetics2024

CAMDA 2023: Finding patterns in urban microbiomes.

Haydeé Contreras-Peruyero, Imanol Nuñez, Mirna Vazquez-Rosas-Landa, Daniel Santana-Quinteros, Antón Pashkov, Mario E Carranza-Barragán, Rafael Perez-Estrada, Shaday Guerrero-Flores, Eugenio Balanzario, Víctor Muñiz Sánchez and 3 more

Abstract read
In one paragraph

Article in Frontiers in genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

13 authors.

Haydeé Contreras-PeruyeroCentro de Ciencias Matemáticas, Universidad Nacional Autónoma de México, Morelia, Mexico.
Imanol NuñezCentro de Investigación en Matemáticas, A.C., Guanajuato, Mexico.
Mirna Vazquez-Rosas-LandaInstituto de Ciencias del Mar y Limnologa, Universidad Nacional Autónoma de México, Ciudad de México, Mexico.
Daniel Santana-QuinterosCentro de Ciencias Matemáticas, Universidad Nacional Autónoma de México, Morelia, Mexico.
Antón PashkovEscuela Nacional de Estudios Superiores unidad Morelia, Universidad Nacional Autónoma de México, Morelia, Mexico.
Mario E Carranza-BarragánCentro de Investigación en Matemáticas, A.C., Guanajuato, Mexico.
Rafael Perez-EstradaCentro de Ciencias Matemáticas, Universidad Nacional Autónoma de México, Morelia, Mexico.
Shaday Guerrero-FloresCentro de Ciencias Matemáticas, Universidad Nacional Autónoma de México, Morelia, Mexico.
Eugenio BalanzarioCentro de Ciencias Matemáticas, Universidad Nacional Autónoma de México, Morelia, Mexico.
Víctor Muñiz SánchezCentro de Investigación en Matemáticas, A.C., Monterrey, Mexico.
Miguel NakamuraCentro de Investigación en Matemáticas, A.C., Guanajuato, Mexico.
L Leticia Ramírez-RamírezCentro de Investigación en Matemáticas, A.C., Guanajuato, Mexico.
Nelly Sélem-MojicaCentro de Ciencias Matemáticas, Universidad Nacional Autónoma de México, Morelia, Mexico.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Critical Assessment of Massive Data Analysis (CAMDA) addresses the complexities of harnessing Big Data in life sciences by hosting annual competitions that inspire research groups to develop innovative solutions. In 2023, the Forensic Challenge focused on identifying the city of origin for 365 metagenomic samples collected from public transportation systems and identifying associations between bacterial distribution and other covariates. For microbiome classification, we incorporated both taxonomic and functional annotations as features. To identify the most informative Operational Taxonomic Units, we selected features by fitting negative binomial models. We then implemented supervised models conducting 5-fold cross-validation (CV) with a 4:1 training-to-validation ratio. After variable selection, which reduced the dataset to fewer than 300 OTUs, the Support Vector Classifier achieved the highest F1 score (0.96). When using functional features from MIFASER, the Neural Network model outperformed other models. When considering climatic and demographic variables of the cities, Dirichlet regression over

Indexed as

CAMDADirichlet regressionforensic metagenomicsfunctional annotationmachine learningMetaSUBnegative binomial modelsvariable selection

Identifiers

PMID39655221
PMCPMC11625776

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.