机器学习用于多组体的血液癌症的表征:系统性审查
Sultan Qalit Alhamrani1,2, Graham Roy Ball3,4, Ahmed A El-Sherif5
1Tabuk Poison Control and Forensic Medicinal Chemistry Center, Ministry of Health, Tabuk 47915, Saudi Arabia.
Cells
|September 13, 2025
概括
人工智能 (AI) 和机器学习 (ML) 显示出使用多omics数据来表征血液癌症的前景. 然而,验证,解释性和标准化方面的挑战需要在临床使用中解决.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 在瘤学瘤学.
背景情况:
- 血液性恶性瘤代表着一个重要的未满足的临床需求.
- 人工智能 (AI) 和机器学习 (ML) 是分析复杂生物数据的强大工具.
- 多omics数据为了解疾病机制提供了全面的观点.
研究的目的:
- 系统地审查AI和ML对多omics数据的应用,以进行血液性恶性瘤的分子特征.
- 评估这些方法的性能,可解释性,可复制性和伦理考虑.
主要方法:
- 从2015年1月到2024年12月,在主要数据库 (PubMed,Embase,IEEE Xplore,Web of Science) 进行系统的文献搜索.
- 包括来自2847个记录的89项研究,数据提取和评估侧重于关键质量指标.
- 对AI/ML方法 (SVM,随机森林,深度学习) 和多omics集成的分析.
主要成果:
- 研究主要集中在急性髓性白血病,急性淋巴细胞白血病和多发性髓瘤.
- 深度学习模型实现了高诊断性能 (AUC 0.91),但解释能力有限.
- 31项研究报告了外部验证,19项研究报告了可解释性.
结论:
- 人工智能和ML证明了血液恶性瘤分子特征的潜力.
- 在AI/ML方法的外部验证,可解释性和标准化方面存在差距.
- 未来的努力应优先考虑可靠的验证,可解释的模型和标准化的临床翻译报告.
关键词:
普里斯玛是什么意思 普里斯玛是什么意思人工智能的人工智能是人工智能.伦理学 伦理 伦理学可以解释性的解释性.基因组学就是基因组学.血液学的恶性瘤.机器学习是机器学习.分子特征的分子表征.多主题整合多主题整合.蛋白质组学是什么意思可复制性的可复制性系统性审查 系统性审查翻译学 翻译学 翻译学 翻译学更多相关视频
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