Evidence map›Paper›PMID 40565220›Full record

ArticleInternational journal of molecular sciences2025

Evaluating Genetic Regulators of MicroRNAs Using Machine Learning Models.

Mert Cihan, Uchenna Alex Anyaegbunam, Steffen Albrecht, Miguel A Andrade-Navarro, Maximilian Sprang

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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.

Mert CihanInstitute of Organismic and Molecular Evolution, Faculty of Biology, Johannes Gutenberg University Mainz, 55128 Mainz, Germany.
Uchenna Alex AnyaegbunamInstitute of Organismic and Molecular Evolution, Faculty of Biology, Johannes Gutenberg University Mainz, 55128 Mainz, Germany.ORCID 0009-0003-5732-8103
Steffen AlbrechtDepartment of General Practice and Primary Health Care, Faculty of Medical and Health Sciences (FMHS), The University of Auckland, Auckland 1023, New Zealand.
Miguel A Andrade-NavarroInstitute of Organismic and Molecular Evolution, Faculty of Biology, Johannes Gutenberg University Mainz, 55128 Mainz, Germany.ORCID 0000-0001-6650-1711
Maximilian SprangInstitute of Organismic and Molecular Evolution, Faculty of Biology, Johannes Gutenberg University Mainz, 55128 Mainz, Germany.ORCID 0000-0002-8438-4747

Funding

Federal Ministry of Education and Research 03ZU1202ABFederal Ministry of Education and Research 03ZU1202ECFederal Ministry of Education and Research MSCoreSys
6 · The paper itself

Abstract

This study explores the genetic regulators of microRNAs (miRNAs) using a set of machine learning models to predict miRNA expression levels from gene expression data. Employing machine learning, we accurately predicted the expression of 353 human miRNAs (R

Indexed as

Gene Expression RegulationGene Regulatory NetworksMachine LearningMicroRNAsComputational BiologyGene Expression ProfilingHumansMicroRNAsfunctional genomicsgene expression modelingmachine learningmicroRNAregulatory networks

Identifiers

PMID40565220
PMCPMC12193497

What Socratic 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 Socratic graph.