Protocol for pro-inflammatory microRNA motif discovery using machine learning.

Chien-Yu Lin, Boyang Ren, Shiming Yang, Rosemary Kozar, Lin Zou, Brittney Williams, Wei Chao, Peter Hu

Abstract read

In one paragraph

Article in STAR protocols, 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

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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

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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

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No citing paper in PubMed yet.

4 · The record

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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

8 authors.

Chien-Yu LinCenter for Shock, Trauma and Anesthesiology Research, University of Maryland School of Medicine, Baltimore, MD, USA.

Boyang RenCenter for Shock, Trauma and Anesthesiology Research, University of Maryland School of Medicine, Baltimore, MD, USA.

Shiming YangCenter for Shock, Trauma and Anesthesiology Research, University of Maryland School of Medicine, Baltimore, MD, USA.

Rosemary KozarCenter for Shock, Trauma and Anesthesiology Research, University of Maryland School of Medicine, Baltimore, MD, USA.

Lin ZouCenter for Shock, Trauma and Anesthesiology Research, University of Maryland School of Medicine, Baltimore, MD, USA.

Brittney WilliamsCenter for Shock, Trauma and Anesthesiology Research, University of Maryland School of Medicine, Baltimore, MD, USA.

Wei ChaoCenter for Shock, Trauma and Anesthesiology Research, University of Maryland School of Medicine, Baltimore, MD, USA. Electronic address: wchao@som.umaryland.edu.

Peter HuCenter for Shock, Trauma and Anesthesiology Research, University of Maryland School of Medicine, Baltimore, MD, USA. Electronic address: ph@som.umaryland.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Here, we present a protocol to identify nucleotide motifs that predict the pro-inflammatory property of microRNAs (miRNAs) using machine learning. We describe steps for cell culture, miRNA transfection, pro-inflammatory classification, and k-mer discovery. We detail procedures for combining in vitro macrophage assays with exhaustive motif searches and least absolute shrinkage and selection operator (LASSO) regression to define nucleotide sequence features that distinguish pro-inflammatory miRNAs. This workflow enables systematic motif discovery and biomarker prioritization directly from miRNA sequences, streamlining translational applications without extensive functional screening. For complete details on the use and execution of this protocol, please refer to Ren et al.

Indexed as

Computational BiologyInflammationMachine LearningMicroRNAsNucleotide MotifsAnimalsHumansMacrophagesMicroRNAsBioinformaticsCell-based AssaysCell BiologyCell cultureGene ExpressionImmunologyMolecular BiologySequence analysis

Identifiers

PMID41894396

DOI10.1016/j.xpro.2026.104467

PMCPMC13053980

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LicenceCC BY-NC-ND

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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.