Advancing miRNA cancer research through artificial intelligence: from biomarker discovery to therapeutic targeting.

IF 2.8 4区 医学 Q2 ONCOLOGY
Raghu Aswathy, Varghese Angel Chalos, Kanagaraj Suganya, Sundaravadivelu Sumathi
{"title":"Advancing miRNA cancer research through artificial intelligence: from biomarker discovery to therapeutic targeting.","authors":"Raghu Aswathy, Varghese Angel Chalos, Kanagaraj Suganya, Sundaravadivelu Sumathi","doi":"10.1007/s12032-024-02579-z","DOIUrl":null,"url":null,"abstract":"<p><p>MicroRNAs (miRNAs), a class of small non-coding RNAs, play a vital role in regulating gene expression at the post-transcriptional level. Their discovery has profoundly impacted therapeutic strategies, particularly in cancer treatment, where RNA therapeutics, including miRNA-based targeted therapies, have gained prominence. Advances in RNA sequencing technologies have facilitated a comprehensive exploration of miRNAs-from fundamental research to their diagnostic and prognostic potential in various diseases, notably cancers. However, the manual handling and interpretation of vast RNA datasets pose significant challenges. The advent of artificial intelligence (AI) has revolutionized biological research by efficiently extracting insights from complex data. Machine learning algorithms, particularly deep learning techniques are effective for identifying critical miRNAs across different cancers and developing prognostic models. Moreover, the integration of AI has led to the creation of comprehensive miRNA databases for identifying mRNA and gene targets, thus facilitating deeper understanding and application in cancer research. This review comprehensively examines current developments in the application of machine learning techniques in miRNA research across diverse cancers. We discuss their roles in identifying biomarkers, elucidating miRNA targets, establishing disease associations, predicting prognostic outcomes, and exploring broader AI applications in cancer research. This review aims to guide researchers in leveraging AI techniques effectively within the miRNA field, thereby accelerating advancements in cancer diagnostics and therapeutics.</p>","PeriodicalId":18433,"journal":{"name":"Medical Oncology","volume":"42 1","pages":"30"},"PeriodicalIF":2.8000,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s12032-024-02579-z","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

MicroRNAs (miRNAs), a class of small non-coding RNAs, play a vital role in regulating gene expression at the post-transcriptional level. Their discovery has profoundly impacted therapeutic strategies, particularly in cancer treatment, where RNA therapeutics, including miRNA-based targeted therapies, have gained prominence. Advances in RNA sequencing technologies have facilitated a comprehensive exploration of miRNAs-from fundamental research to their diagnostic and prognostic potential in various diseases, notably cancers. However, the manual handling and interpretation of vast RNA datasets pose significant challenges. The advent of artificial intelligence (AI) has revolutionized biological research by efficiently extracting insights from complex data. Machine learning algorithms, particularly deep learning techniques are effective for identifying critical miRNAs across different cancers and developing prognostic models. Moreover, the integration of AI has led to the creation of comprehensive miRNA databases for identifying mRNA and gene targets, thus facilitating deeper understanding and application in cancer research. This review comprehensively examines current developments in the application of machine learning techniques in miRNA research across diverse cancers. We discuss their roles in identifying biomarkers, elucidating miRNA targets, establishing disease associations, predicting prognostic outcomes, and exploring broader AI applications in cancer research. This review aims to guide researchers in leveraging AI techniques effectively within the miRNA field, thereby accelerating advancements in cancer diagnostics and therapeutics.

通过人工智能推进miRNA癌症研究:从生物标志物发现到治疗靶向。
MicroRNAs (miRNAs)是一类小的非编码rna,在转录后水平调控基因表达中起着至关重要的作用。他们的发现深刻地影响了治疗策略,特别是在癌症治疗中,RNA治疗,包括基于mirna的靶向治疗,已经得到了突出。RNA测序技术的进步促进了对mirna的全面探索——从基础研究到其在各种疾病(尤其是癌症)中的诊断和预后潜力。然而,人工处理和解释大量RNA数据集带来了重大挑战。人工智能(AI)的出现通过有效地从复杂数据中提取见解,彻底改变了生物学研究。机器学习算法,特别是深度学习技术,对于识别不同癌症中的关键mirna和开发预后模型是有效的。此外,人工智能的整合还创建了用于识别mRNA和基因靶点的综合miRNA数据库,从而促进了对癌症研究的更深入理解和应用。这篇综述全面检查了机器学习技术在不同癌症miRNA研究中的应用的最新进展。我们讨论了它们在识别生物标志物,阐明miRNA靶点,建立疾病关联,预测预后结果以及探索AI在癌症研究中的更广泛应用方面的作用。这篇综述旨在指导研究人员在miRNA领域有效利用人工智能技术,从而加速癌症诊断和治疗的进展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Medical Oncology
Medical Oncology 医学-肿瘤学
CiteScore
4.20
自引率
2.90%
发文量
259
审稿时长
1.4 months
期刊介绍: Medical Oncology (MO) communicates the results of clinical and experimental research in oncology and hematology, particularly experimental therapeutics within the fields of immunotherapy and chemotherapy. It also provides state-of-the-art reviews on clinical and experimental therapies. Topics covered include immunobiology, pathogenesis, and treatment of malignant tumors.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信