Evidence map›Paper›PMID 25960872›Full record

ArticleHealth information science and systems2015

Lactococcus garvieae: a small bacteria and a big data world.

Guillermo López-Campos, Mónica Aguado-Urda, María Mar Blanco, Alicia Gibello, María Teresa Cutuli, Victoria López-Alonso, Fernando Martín-Sánchez, José F Fernández-Garayzábal

Open access · hybridAbstract read
In one paragraph

Article in Health information science and systems, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 38% 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

1 citing paper in PubMed, 8 citations in OpenAlex.

  1. Studies on the effect ofInternational journal of veterinary science and medicine · 2018
    Article
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

8 authors at 3 institutions in 2 countries.

Guillermo López-CamposHealth and Biomedical Informatics Centre (HABIC), The University of Melbourne, Melbourne, Victoria, 3010, Australia.
Mónica Aguado-UrdaFaculty of Veterinary Sciences, Department of Animal Health, Complutense University, Madrid, 28040, Spain.
María Mar BlancoFaculty of Veterinary Sciences, Department of Animal Health, Complutense University, Madrid, 28040, Spain.
Alicia GibelloFaculty of Veterinary Sciences, Department of Animal Health, Complutense University, Madrid, 28040, Spain.
María Teresa CutuliFaculty of Veterinary Sciences, Department of Animal Health, Complutense University, Madrid, 28040, Spain.
Victoria López-AlonsoComputational Biology Unit, National Institute of Health "Carlos III", Madrid, 28220, Spain.
Fernando Martín-SánchezHealth and Biomedical Informatics Centre (HABIC), The University of Melbourne, Melbourne, Victoria, 3010, Australia.
José F Fernández-GarayzábalFaculty of Veterinary Sciences, Department of Animal Health, Complutense University, Madrid, 28040, Spain.
Universidad Complutense de Madrid · ESUniversity of Melbourne · AUInstituto de Salud Carlos III · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo describe the importance of bioinformatics tools to analyze the big data yielded from new "omics" generation-methods, with the aim of unraveling the biology of the pathogen bacteria Lactococcus garvieae.

methodsThe paper provides the vision of the large volume of data generated from genome sequences, gene expression profiles by microarrays and other experimental methods that require biomedical informatics methods for management and analysis.

resultsThe use of biomedical informatics methods improves the analysis of big data in order to obtain a comprehensive characterization and understanding of the biology of pathogenic organisms, such as L. garvieae.

conclusionsThe "Big Data" concepts of high volume, veracity and variety are nowadays part of the research in microbiology associated with the use of multiple methods in the "omic" era. The use of biomedical informatics methods is a requisite necessary to improve the analysis of these data.

Indexed as

Big DataGene expressionGenomicsLactococcus garvieae

Identifiers

PMID25960872
PMCPMC4416232
OpenAlexW2101717896

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.