用机器学习方法对人类多能干细胞的表型识别进行形态信号处理
Ekaterina Vedeneeva1, Vitaly Gursky2,3, Maria Samsonova1
1Department of Physics and Mechanics & Mathematical Biology and Bioinformatics Laboratory, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia.
Biomedicines
|November 25, 2023
概括
选择人类多能干细胞克隆用于再生医学可以自动化. 机器学习模型使用结合的细胞和殖民地形态学准确预测细胞质量,改善医疗应用的质量控制.
科学领域:
- 干细胞生物学 干细胞生物学
- 再生医学是一种再生医学.
- 生物信息学是一种生物信息学.
背景情况:
- 人类多能干细胞 (hPSCs) 对于再生医学至关重要,因为它们具有自我更新和分化潜力.
- 高质量的hPSC克隆的自动选择是必不可少的,但具有挑战性.
- 需要使用无标签的,非侵入性的方法来评估hPSC殖民地表型.
研究的目的:
- 开发和评估机器学习模型来分类hPSC殖民地表型 ("好"或"坏").
- 为了确定最佳的形态参数和分类方法来预测hPSC质量.
- 建立在医疗环境中自动化hPSC质量控制的基础.
主要方法:
- 利用相对比成像从hPSC殖民地提取形态参数.
- 应用机器学习算法,包括人工神经网络,物流回归和随机森林.
- 使用细胞形态学,殖民地形态学和组合参数比较分类准确度.
主要成果:
- 使用细胞形态的模型达到67%的准确性 (人工神经网络).
- 使用殖民地形态的模型达到75%的准确性 (逻辑回归).
- 将细胞和殖民地形态与随机森林结合起来,获得了99%的准确性.
结论:
- 细胞和殖民地形态特征都是准确的hPSC表型预测所必需的.
- 整合各种形态数据的机器学习模型显著增强了hPSC的自动化质量控制.
- 这些发现支持在医疗应用中开发用于hPSC选择的自动化系统.
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