Evidence map›Paper›PMID 42681317›Full record

ReviewMethods in molecular biology (Clifton, N.J.)2026

miRNA Target Prediction: An Overview of the Past and Current Tools.

Sebastiano Di Bella, Francesco Orilio, Giovanni Nigita, Matilde Todaro, Giorgio Stassi

Abstract readReview
PubMed Publisher
In one paragraph

Review in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Sebastiano Di Bella *Department of Precision Medicine in Medical, Surgical and Critical Care, University of Palermo, Palermo, Italy. sebastiano.dibella@unipa.it.
Francesco Orilio *Department of Precision Medicine in Medical, Surgical and Critical Care, University of Palermo, Palermo, Italy.
Giovanni NigitaDivision of Medical Oncology, Department of Internal Medicine, College of Medicine, The Arthur G. James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.
Matilde TodaroDepartment of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialities (PROMISE), University of Palermo, Palermo, Italy.
Giorgio StassiDepartment of Precision Medicine in Medical, Surgical and Critical Care, University of Palermo, Palermo, Italy. giorgio.stassi@unipa.it.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNAs (miRNAs) are among the most studied molecules in recent years, and since their discovery, many miRNAs have been identified across various species. As members of the non-coding RNA family, miRNAs are key players in post-transcriptional gene regulation. These molecules can inhibit translation or promote degradation of messenger RNA (mRNA) by binding to the 3' untranslated region (UTR) of mRNA, thereby influencing almost all biological processes. To identify a miRNA's biological role, it is essential to predict the target sites to which it binds, a goal made possible through bioinformatics tools. This chapter discusses the bioinformatics tools commonly used for this purpose. Also, it analyzes the main factors considered in target prediction, such as seed match, free energy, conservation, site accessibility, multiple binding site contribution, and machine learning and deep learning approaches. Understanding the principles underlying these predictive methodologies is crucial for advancing one's biological research on miRNAs.

Indexed as

Computational BiologyMicroRNAsRNA, Messenger3' Untranslated RegionsAnimalsBinding SitesHumansMachine LearningPrediction AlgorithmsSoftware3' Untranslated RegionsMicroRNAsRNA, MessengerBioinformatic toolsConservationFree energyMachine learningMicroRNASeed matchSite accessibilityTarget prediction

Identifiers

PMID42681317

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.