Evidence map›Paper›PMID 29765080›Full record

ArticleScientific reports2018

Distinguishing mirtrons from canonical miRNAs with data exploration and machine learning methods.

Grzegorz Rorbach, Olgierd Unold, Bogumil M Konopka

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

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
2.1field-weighted citation impact, top 14% 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

23 citing papers in PubMed, 44 citations in OpenAlex.

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  10. MicroRNA Databases and Tools.Methods in molecular biology (Clifton, N.J.) · 2022
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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

3 authors at 2 institutions in 1 country.

Grzegorz RorbachDepartment of Computer Engineering, Faculty of Electronics, Wroclaw University of Science and Technology, Wroclaw, Poland.
Olgierd UnoldDepartment of Computer Engineering, Faculty of Electronics, Wroclaw University of Science and Technology, Wroclaw, Poland.
Bogumil M KonopkaDepartment of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wroclaw, Poland. bogumil.konopka@pwr.edu.pl.
AGH University of Krakow · PLWrocław University of Science and Technology · PL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mirtrons are non-canonical microRNAs encoded in introns the biogenesis of which starts with splicing. They are not processed by Drosha and enter the canonical pathway at the Exportin-5 level. Mirtrons are much less evolutionary conserved than canonical miRNAs. Due to the differences, canonical miRNA predictors are not applicable to mirtron prediction. Identification of differences is important for designing mirtron prediction algorithms and may help to improve the understanding of mirtron functioning. So far, only simple, single-feature comparisons were reported. These are insensitive to complex feature relations. We quantified miRNAs with 25 features and showed that it is impossible to distinguish the two miRNA species using simple thresholds on any single feature. However, when using the Principal Component Analysis mirtrons and canonical miRNAs are grouped separately. Moreover, several methodologically diverse machine learning classifiers delivered high classification performance. Using feature selection algorithms we found features (e.g. bulges in the stem region), previously reported divergent in two classes, that did not contribute to improving classification accuracy, which suggests that they are not biologically meaningful. Finally, we proposed a combination of the most important features (including Guanine content, hairpin free energy and hairpin length) which convey a specific pattern, crucial for identifying mirtrons.

Indexed as

AlgorithmsAnimalsBase CompositionComputational BiologyDatabases, GeneticHumansIntronsMachine LearningMiceMicroRNAsModels, MolecularNucleic Acid ConformationPrincipal Component AnalysisMicroRNAs

Identifiers

PMID29765080
PMCPMC5953923
OpenAlexW2804432360

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.