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ZENODO
Dataset . 2021
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
Smithsonian figshare
Dataset . 2021
License: CC BY
ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
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Negative instances for detecting LTR-Retrotransposons using Machine Learning

Authors: Simon Orozco Arias (10153612); Mariana S. Candamil (10153615); Paula A. Jaimes (10153618); Johan S. Piña (10153621); Reinel Tabares-Soto (10153624); Romain Guyot (166092); Gustavo Isaza (4386541);

Negative instances for detecting LTR-Retrotransposons using Machine Learning

Abstract

This dataset is composed of genomic features other than LTR-Retrotransposons (LTR_RTs), such as coding sequences (CDS), different types of RNA (e.g., mRNA, tRNA, non-coding RNA, among others), and other types of transposable elements that do not belong to LTR-RTs (e.g., TEs Class II, PLEs, DIRs, LINEs, and SINEs) from the same plant species contained in InpactorDB (DOI 10.5281/zenodo.4386316). These additional transposable element sequences were available in databases such as PGSB PlantsDB, Repbase (v. 20.05, 2017), RepetDB, Ensembl Plants, and JGI (Joint Genome Institute).

Keywords

plant genomes, Evolutionary Biology, k-mer based method., Ecology, LTR retrotransposons, detection, Plant Biology, Computational Biology, Microbiology, free-alignment approach, Infectious Diseases, machine learning, Virology, k-mer based method, Genetics, transposable elements, Molecular Biology, Developmental Biology, Biological Sciences not elsewhere classified

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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