Evidence map›Paper›PMID 31307371›Full record

ArticleBMC bioinformatics2019

RAFTS

Bruno Thiago de Lima Nichio, Aryel Marlus Repula de Oliveira, Camilla Reginatto de Pierri, Leticia Graziela Costa Santos, Alexandre Quadros Lejambre, Ricardo Assunção Vialle, Nilson Antônio da Rocha Coimbra, Dieval Guizelini, Jeroniza Nunes Marchaukoski, Fabio de Oliveira Pedrosa and 1 more

Abstract read
In one paragraph

Article in BMC bioinformatics, 2019. 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. Article
  2. Article
  3. 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

11 authors.

Bruno Thiago de Lima NichioLaboratory of Bioinformatics, Professional and Technical Education Sector from the Federal University of Paraná, Curitiba, PR, Brazil.
Aryel Marlus Repula de OliveiraLaboratory of Bioinformatics, Professional and Technical Education Sector from the Federal University of Paraná, Curitiba, PR, Brazil.
Camilla Reginatto de PierriLaboratory of Bioinformatics, Professional and Technical Education Sector from the Federal University of Paraná, Curitiba, PR, Brazil.
Leticia Graziela Costa SantosLaboratory of Bioinformatics, Professional and Technical Education Sector from the Federal University of Paraná, Curitiba, PR, Brazil.
Alexandre Quadros LejambreLaboratory of Bioinformatics, Professional and Technical Education Sector from the Federal University of Paraná, Curitiba, PR, Brazil.
Ricardo Assunção VialleLaboratory of Bioinformatics, Professional and Technical Education Sector from the Federal University of Paraná, Curitiba, PR, Brazil.
Nilson Antônio da Rocha CoimbraLaboratory of Bioinformatics, Professional and Technical Education Sector from the Federal University of Paraná, Curitiba, PR, Brazil.
Dieval GuizeliniLaboratory of Bioinformatics, Professional and Technical Education Sector from the Federal University of Paraná, Curitiba, PR, Brazil.
Jeroniza Nunes MarchaukoskiLaboratory of Bioinformatics, Professional and Technical Education Sector from the Federal University of Paraná, Curitiba, PR, Brazil.
Fabio de Oliveira PedrosaLaboratory of Bioinformatics, Professional and Technical Education Sector from the Federal University of Paraná, Curitiba, PR, Brazil.
Roberto Tadeu RaittzLaboratory of Bioinformatics, Professional and Technical Education Sector from the Federal University of Paraná, Curitiba, PR, Brazil. raittz@ufpr.br.ORCID http://orcid.org/0000-0002-5271-991X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundClustering methods are essential to partitioning biological samples being useful to minimize the information complexity in large datasets. Tools in this context usually generates data with greed algorithms that solves some Data Mining difficulties which can degrade biological relevant information during the clustering process. The lack of standardization of metrics and consistent bases also raises questions about the clustering efficiency of some methods. Benchmarks are needed to explore the full potential of clustering methods - in which alignment-free methods stand out - and the good choice of dataset makes it essentials.

resultsHere we present a new approach to Data Mining in large protein sequences datasets, the Rapid Alignment Free Tool for Sequences Similarity Search to Groups (RAFTS

conclusionIn general, RAFTS

Indexed as

SoftwareAlgorithmsCluster AnalysisDatabases, ProteinData MiningProteinsProteins

Identifiers

PMID31307371
PMCPMC6631606

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.