Evidence map›Paper›PMID 39747792›Full record

ArticleMetabolic brain disease2025

Selective diagnostics of Amyotrophic Lateral Sclerosis, Alzheimer's and Parkinson's Diseases with machine learning and miRNA.

Juhyeok Lee, Valentina L Kouznetsova, Santosh Kesari, Igor Tsigelny

Abstract read
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In one paragraph

Article in Metabolic brain disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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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

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

4 authors.

Juhyeok LeeCureScience Institute Internship Program, San Diego, CA, 92121, USA.
Valentina L KouznetsovaSan Diego Supercomputer Center, UC San Diego, San Diego, CA, 92093, USA.
Santosh KesariPacific Neuroscience Institute, Santa Monica, CA, 90404, USA.
Igor TsigelnySan Diego Supercomputer Center, UC San Diego, San Diego, CA, 92093, USA. itsigeln@ucsd.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The diagnosis of neurological diseases can be expensive, invasive, and inaccurate, as it is often difficult to distinguish between different types of diseases with similar motor symptoms. However, the dysregulation of miRNAs can be used to create a robust machine-learning model for a reliable diagnosis of neurological diseases. We used miRNA sequence descriptors and gene target data to create machine-learning models that can be used as diagnostic tools. The top-performing machine-learning models, trained on filtered miRNA datasets for Amyotrophic Lateral Sclerosis, Alzheimer's and Parkinson's Diseases of this research yielded 94, 97, and 96, percent accuracies, respectively. Analysis of dysregulated miRNA in neurological diseases elucidated novel biomarkers that could be used to diagnose and distinguish between the diseases. Machine-learning models developed using sequence and gene target descriptors of miRNA biomarkers can achieve favorable accuracies for disease classification and attain a robust discerning capability of neurological diseases.

Indexed as

Alzheimer DiseaseAmyotrophic Lateral SclerosisBiomarkersMachine LearningMicroRNAsParkinson DiseaseHumansBiomarkersMicroRNAsAlzheimer’s diseaseAmyotrophic lateral sclerosisMachine learningmiRNANeurological diseaseParkinson’s disease

Identifiers

PMID39747792

What OpenQuestion holds

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Registered trials

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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.