机器学习在MicroRNA诊断中的应用:当前观点。

Chrysanthos D Christou, Angelos C Mitsas, Ioannis Vlachavas, Georgios Tsoulfas
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引用次数: 2

摘要

MicroRNAs是一种小的非编码rna,在调节mRNA的翻译和降解中起着关键作用,并与许多疾病有关。人工智能(AI)是一个不断发展的相互关联的领域集群,机器学习(ML)作为最突出的人工智能领域之一脱颖而出,在人类生活的几乎每个方面都有大量的应用。机器学习可以被定义为从过去的数据中学习以预测未来数据的计算机算法。本文综述了目前基于microrna的ML模型在医疗保健中的应用。大多数已确定的研究调查了基于microrna的ML模型在癌症管理中的作用,特别是胃癌(最大诊断准确率(Accmax): 94%)、胰腺癌(Accmax: 93%)、结直肠癌(Accmax: 100%)、乳腺癌(Accmax: 97%)、卵巢癌、颈部鳞状细胞癌、肝癌、肺癌(Accmax: 100%)和黑色素瘤。除癌症外,基于microrna的ML模型已应用于大量其他疾病,包括溃疡性结肠炎(Accmax: 92.8%)、子宫内膜异位症、妊娠糖尿病(Accmax: 86%)、听力损失、缺血性中风、冠心病(Accmax: 96%)、结核病、肺动脉高压(Accmax: 83%)、痴呆(Accmax: 82.9%)、终末期肾病患者的主要心血管事件和酒精依赖(Accmax: 79.1%)。我们的研究结果表明,基于microrna的ML模型的开发可用于提高多种疾病的诊断准确性,同时替代或尽量减少使用更具侵入性的诊断手段(如内窥镜检查)。即使没有预期的那么快,人工智能最终也会渗透到整个医疗行业。人工智能是接受医学固有复杂性的临床实践的关键。因此,人工智能将成为一种现实,医生应该顺应,以避免被淘汰。
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The Use of Machine Learning in MicroRNA Diagnostics: Current Perspectives.

MicroRNAs constitute small non-coding RNAs that play a pivotal role in regulating the translation and degradation of mRNA and have been associated with many diseases. Artificial Intelligence (AI) is an evolving cluster of interrelated fields, with machine learning (ML) standing out as one of the most prominent AI fields, with a plethora of applications in almost every aspect of human life. ML could be defined as computer algorithms that learn from past data to predict future data. This review comprehensively reviews the current applications of microRNA-based ML models in healthcare. The majority of the identified studies investigated the role of microRNA-based ML models in the management of cancer and specifically gastric cancer (maximum diagnostic accuracy (Accmax): 94%), pancreatic cancer (Accmax: 93%), colorectal cancer (Accmax: 100%), breast cancer (Accmax: 97%), ovarian cancer, neck squamous cell carcinoma, liver cancer, lung cancer (Accmax: 100%), and melanoma. Except for cancer, microRNA-based ML models have been applied for a plethora of other diseases, including ulcerative colitis (Accmax: 92.8%), endometriosis, gestational diabetes mellitus (Accmax: 86%), hearing loss, ischemic stroke, coronary heart disease (Accmax: 96%), tuberculosis, pulmonary arterial hypertension (Accmax: 83%), dementia (Accmax: 82.9%), major cardiovascular events in end-stage renal disease patients, and alcohol dependence (Accmax: 79.1%). Our findings suggest that the development of microRNA-based ML models could be used to enhance the diagnostic accuracy of a plethora of diseases while at the same time substituting or minimizing the use of more invasive diagnostic means (such as endoscopy). Even not as fast as anticipated, AI will eventually infiltrate the entire healthcare industry. AI is the key to a clinical practice where medicine's inherent complexity is embraced. Therefore, AI will become a reality that physicians should conform with to avoid becoming obsolete.

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