Evidence map›Paper›PMID 31422557›Full record

ArticleMedical & biological engineering & computing2019

The Helitron family classification using SVM based on Fourier transform features applied on an unbalanced dataset.

Rabeb Touati, Afef Elloumi Oueslati, Imen Messaoudi, Zied Lachiri

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Article in Medical & biological engineering & computing, 2019. 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. New methodology for repetitive sequences identification inBiomedical signal processing and control · 2021
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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

4 authors.

Rabeb TouatiLR99ES10 Human Genetics Laboratory, Faculty of Medicine of Tunis (FMT), University of Tunis El Manar, Tunis, Tunisia. Rabeb.touati@enit.utm.tn.ORCID http://orcid.org/0000-0001-5982-7123
Afef Elloumi OueslatiSITI Laboratory, National School of Engineers of Tunis (ENIT), University Tunis El Manar, BP 37, le Belvédère, 1002, Tunis, Tunisia.
Imen MessaoudiSITI Laboratory, National School of Engineers of Tunis (ENIT), University Tunis El Manar, BP 37, le Belvédère, 1002, Tunis, Tunisia.
Zied LachiriSITI Laboratory, National School of Engineers of Tunis (ENIT), University Tunis El Manar, BP 37, le Belvédère, 1002, Tunis, Tunisia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Helitrons are mobile sequences which belong to the class 2 of eukaryotic transposons. Their specificity resides in their mechanism of transposition: the rolling circle mechanism. They play an important role in remodeling proteomes due to their ability to modify existing genes and introducing new ones. A major difficulty in identifying and classifying Helitron families comes from the complex structure, the unspecified length, and the unbalanced appearance number of each Helitron type. The Helitron's recognition is still not solved in literature. The purpose of this paper is to characterize and classify Helitron types using spectral features and support vector machine (SVM) classification technique. Thus, the helitronic DNA is transformed into a numerical form using the FCGS

Indexed as

Databases as TopicFourier AnalysisSupport Vector MachineAnimalsCaenorhabditis elegansChromosomesGenome, HelminthC. elegansFCGS2 codingHelitronsSmoothed Fourier transformSpectral signatureSVM classification

Identifiers

PMID31422557

What OpenQuestion holds

Textmetadata
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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.