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6/18/2026 9:51:21 AM | Browse: 0 | Download: 0
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Received |
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2026-03-09 02:11 |
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Peer-Review Started |
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2026-03-09 02:12 |
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First Decision by Editorial Office Director |
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2026-03-20 10:45 |
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Return for Revision |
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2026-03-20 10:45 |
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Revised |
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2026-04-03 09:00 |
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Publication Fee Transferred |
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2026-04-04 03:44 |
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Second Decision by Editor |
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2026-05-20 02:38 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2026-05-20 07:01 |
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Articles in Press |
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2026-05-20 07:01 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-05-28 08:20 |
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Publish the Manuscript Online |
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2026-06-18 09:51 |
| ISSN |
1949-8462 (online) |
| Open Access |
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Copyright |
©Author(s) (or their employer(s)) 2026. No commercial re-use. See Permissions. Published by Baishideng Publishing Group Inc. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Cardiac & Cardiovascular Systems |
| Manuscript Type |
Systematic Reviews |
| Article Title |
Machine learning integration in microRNA-based markers for cardiovascular diseases: A systematic review
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| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Apurva Popat, Srinivasulu Sathipati and Param Sharma |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Apurva Popat, MD, Department of Cardiology, Sanford Health, Marshfield Clinic, 1000 N Oak Ave, Marshfield, WI, 54449, Marshfield, WI 54449, United States. drapurvapopat@gmail.com |
| Key Words |
MicroRNAs; Cardiovascular diseases; Machine learning; Coronary artery disease; Acute myocardial infarction |
| Core Tip |
Machine learning integration with miRNA profiles demonstrates promising discriminative performance (area under the curve-receiver operating characteristic often exceeding 0.80) for diagnosing and differentiating subtypes of cardiovascular diseases, including acute myocardial infarction, coronary artery disease, hypertension (HTN) subtypes, post-operative atrial fibrillation after coronary artery bypass grafting, and pulmonary HTN. However, the current evidence is limited by small-to-moderate sample sizes, predominant reliance on internal validation only, and lack of assessment of calibration, incremental value over established biomarkers (e.g., troponin), and standardization of pre-analytical and analytical methods. Large-scale prospective studies with external validation and rigorous standardization are essential before these approaches can be translated into routine clinical practice. |
| Publish Date |
2026-06-18 09:51 |
| Citation |
Popat A, Sathipati S, Sharma P. Machine learning integration in microRNA-based markers for cardiovascular diseases: A systematic review. World J Cardiol 2026; 18(6): 120747
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| URL |
https://www.wjgnet.com/1949-8462/full/v18/i6/120747.htm |
| DOI |
https://doi.org/10.4330/wjc.120747 |
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