文献综述:基于机器学习的干细胞调查
1Department of Electrical Engineering and Information Systems, University of Pannonia, Veszprem, Hungary.
Annals of translational medicine
|June 24, 2024
概括
机器学习 (ML) 有助于干细胞 (SC) 观察临床解决方案. 这次审查强调了ML的ML.
科学领域:
- 生物医学工程 生物医学工程
- 计算生物学 计算生物学
- 再生医学是一种再生医学.
背景情况:
- 干细胞 (SC) 对人类器官和临床应用至关重要.
- 准确的SC分类和治疗药物的预测仍然具有挑战性.
- 机器学习 (ML) 为基于SC的诊断和疾病识别提供了有效的解决方案.
研究的目的:
- 审查临床解决方案的干细胞 (SC) 调查中的ML应用.
- 从临床角度评估ML技术在SC观察中的有效性.
- 确定在SC研究中对ML的当前挑战和未来方向.
主要方法:
- 从五个主要科学数据库中对15篇文章进行系统审查.
- 关键词侧重于ML,干细胞和临床应用.
- 在十个关键方面提取和总结数据,包括ML分类器和性能.
主要成果:
- 大多数研究都使用卷积神经网络和支持向量机来识别疾病.
- 关键的局限性包括狭窄的焦点,小的数据集和不充分的验证.
- 没有单一的ML技术是SC分类或预测的绝对最佳.
结论:
- 需要进一步的研究来开发和完善用于SC分析的ML解决方案.
- 扩大数据集和采用先进的评估流程至关重要.
- 整合不同的ML技术可以增强基于SC的临床应用.
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