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机器学习用于催化非编码RNA在研究和临床实践中的整合

David de Gonzalo-Calvo¹, Kanita Karaduzovic-Hadziabdic², Louise Torp Dalgaard³

¹Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain; CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain.

|July 19, 2024

概括

人工智能 (AI) 和机器学习 (ML) 可以识别疾病的非编码RNA (ncRNA) 生物标志物. 这篇评论探讨了AI/ML.

科学领域:

生物化学生物化学
基因组学就是基因组学.
生物信息学是一种生物信息学.

背景情况:

人类转录组富含非编码RNAs (ncRNAs),它们调节生物过程,并作为潜在的生物标志物.
将ncRNA数据与临床和多核信息相结合,对于全面了解疾病至关重要.
人工智能 (AI) 和机器学习 (ML) 对于分析复杂的ncRNA数据越来越重要.

研究的目的:

提供AI/ML应用在识别临床相关的ncRNA生物标志物的概述.
探索使用AI/ML来破译ncRNA相关的病原遗传机制的潜力和挑战.
检查ncRNA研究和医疗保健中AI的方法,概念和伦理方面的考虑.

主要方法:

对应用在ncRNA研究中的AI/ML技术的当前文献的综述.
讨论使用ncRNA配置文件的多组数据集成策略.
分析人工智能驱动生物标志物发现的挑战和伦理影响.

主要成果:

人工智能/ML方法为发现新型ncRNA生物标志物提供了显著的潜力.
挑战包括数据集成,方法限制和复杂模型的解释性.
对于在医疗保健中负责任地实施人工智能,道德考虑至关重要.

结论:

人工智能/ML是推动ncRNA生物标志物发现和了解疾病机制的强大工具.
解决当前的局限性和伦理问题对于临床翻译至关重要.
这个领域对个性化医疗和改善患者治疗结果充满希望.

关键词:

人工智能的人工智能是人工智能.生物标志物生物标志物机器学习机器学习分子路径的分子路径.没有编码的RNA.个性化药物是个性化的药物.

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