Evidence map›Paper›PMID 34122498›Full record

ArticleFrontiers in genetics2021

A Computational Framework for Pattern Detection on Unaligned Sequences: An Application on SARS-CoV-2 Data.

Nikolaos Pechlivanis, Anastasios Togkousidis, Maria Tsagiopoulou, Stefanos Sgardelis, Ilias Kappas, Fotis Psomopoulos

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in genetics, 2021. 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
–field-weighted citation impact, top 96% 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, 0 citations in OpenAlex.

  1. 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

6 authors at 2 institutions in 1 country.

Nikolaos PechlivanisInstitute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece.
Anastasios TogkousidisInstitute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece.
Maria TsagiopoulouInstitute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece.
Stefanos SgardelisDepartment of Ecology, School of Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Ilias KappasDepartment of Genetics, Development and Molecular Biology, School of Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Fotis PsomopoulosInstitute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece.
Centre for Research and Technology Hellas · GRAristotle University of Thessaloniki · GR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The exponential growth of genome sequences available has spurred research on pattern detection with the aim of extracting evolutionary signal. Traditional approaches, such as multiple sequence alignment, rely on positional homology in order to reconstruct the phylogenetic history of taxa. Yet, mining information from the plethora of biological data and delineating species on a genetic basis, still proves to be an extremely difficult problem to consider. Multiple algorithms and techniques have been developed in order to approach the problem multidimensionally. Here, we propose a computational framework for identifying potentially meaningful features based on

Indexed as

feature selectionk-mersphylogeneticsSARS-CoV-2unsupervised learning

Identifiers

PMID34122498
PMCPMC8194296
OpenAlexW3166126515

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.