Evidence map›Paper›PMID 34432279›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2022

Computational Methods for Predicting Mature microRNAs.

Malik Yousef, Alisha Parveen, Abhishek Kumar

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2022. 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. Review
  2. Hepatitis B Virus and microRNAs: A Bioinformatics Approach.International journal of molecular sciences · 2023
    Review
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.

Malik YousefDepartment of Information System, Galilee Digital Health Research Center (GDH), Zefat Academic College, Zefat, Israel.
Alisha ParveenRudolf‑Zenker Institute of Experimental Surgery, Rostock University Medical Center, Rostock, Germany.
Abhishek KumarInstitute of Bioinformatics, Bangalore, India. abhishek@ibioinformatics.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tiny single-stranded noncoding RNAs with size 19-27 nucleotides serve as microRNAs (miRNAs), which have emerged as key gene regulators in the last two decades. miRNAs serve as one of the hallmarks in regulatory pathways with critical roles in human diseases. Ever since the discovery of miRNAs, researchers have focused on how mature miRNAs are produced from precursor mRNAs. Experimental methods are faced with notorious challenges in terms of experimental design, since it is time consuming and not cost-effective. Hence, different computational methods have been employed for the identification of miRNA sequences where most of them labeled as miRNA predictors are in fact pre-miRNA predictors and provide no information about the putative miRNA location within the pre-miRNA. This chapter provides an update and the current state of the art in this area covering various methods and 15 software suites used for prediction of mature miRNA.

Indexed as

Computational BiologyHumansMicroRNAsRNA PrecursorsSoftwareMicroRNAsRNA PrecursorsMachine learningMature miRNAMature miRNA predictionMicro RNARandom forestSupport vector machine

Identifiers

PMID34432279

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.