Unlocking the potential of microRNAs: Machine learning identifies key biomarkers for myocardial infarction diagnosis
Open accessAbstract
Background: MicroRNAs (miRNAs) play a crucial role in regulating adaptive and maladaptive responses in cardiovascular diseases, making them attractive targets for potential biomarkers. However, their potential as novel biomarkers for diagnosing cardiovascular diseases requires systematic evaluation. Methods: In this study, we aimed to identify a key set of miRNA biomarkers using integrated bioinformatics and machine learning analysis. We combined and analyzed three gene expression datasets from the Gene Expression Omnibus (GEO) database, which contains peripheral blood mononuclear cell (PBMC) samples from individuals with myocardial infarction (MI), stable coronary artery disease (CAD), and healthy individuals. Additionally, we selected a set of miRNAs based on their area under the receiver operating characteristic curve (AUC-ROC) for separating the CAD and MI samples. We designed a two-layer architecture for sample classification, in which the first layer isolates healthy samples from unhealthy samples, and the second layer classifies stable CAD and MI samples. We trained different machine learning models using both biomarker sets and evaluated their performance on a test set. Results: We identified hsa-miR-21-3p, hsa-miR-186-5p, and hsa-miR-32-3p as the differentially expressed miRNAs, and a set including hsa-miR-186-5p, hsa-miR-21-3p, hsa-miR-197-5p, hsa-miR-29a-5p, and hsa-miR-296-5p as the optimum set of miRNAs selected by their AUC-ROC. Both biomarker sets could distinguish healthy from not-healthy samples with complete accuracy. The best performance for the classification of CAD and MI was achieved with an SVM model trained using the biomarker set selected by AUC-ROC, with an AUC-ROC of 0.96 and an accuracy of 0.94 on the test data. Conclusions: Our study demonstrated that miRNA signatures derived from PBMCs could serve as valuable novel biomarkers for cardiovascular diseases.
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Samadishadlou et al. [150] achieved an AUC-ROC of 96% and an accuracy of 94%.
“Their model demonstrated an AUC-ROC of 96% and an accuracy of 94%.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [150]
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A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis
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- Authors
- Samadishadlou, M., Rahbarghazi, R., Piryaei, Z., Esmaeili, M., Avcı, Ç. B., Bani, F., & Kavousi, K.
- Affiliations
- Tabriz University of Medical Sciences (IR)
- University of Tehran (IR)
- Ege University (TR)
- Corresponding author
- Bani, Farhad
- Kavousi, Kaveh
- Venue
- Cardiovascular Diabetology
- Published
- 2023-09-11
- Volume
- 22
- Issue
- 1
- Pages
- 247-247
- ISSN
- 1475-2840 (ISSN-L)
- PMC
- PMC10496209
- OpenAlex
- W4386599689
- Cited by
- 26
- References
- 44
- Citations by year
- 2026: 9 · 2025: 10 · 2024: 7
- FWCI
- 3.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Cardiovascular Diabetology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Tabriz University of Medical Sciences (66372)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › MicroRNA in disease regulation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- microRNA, myocardial infarction, biomarkers, stable coronary artery disease, peripheral blood mononuclear cells, microRNA signature, cardiovascular disease diagnosis, support vector machine classification
- MeSH terms
- Machine Learning, Coronary Artery Disease, Humans, Leukocytes, Mononuclear, Myocardial Infarction, Biomarkers, MicroRNAs
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