在标签中:预测未标记图像中的光标签
Eric M Christiansen1, Samuel J Yang1, D Michael Ando1
1Google, Inc., Mountain View, CA 94043, USA.
Cell
|April 17, 2018
概括
在标签 (ISL) 中使用机器学习来从未标记的细胞的传输光图像中预测光标签. 这种计算方法克服了传统方法的局限性,使得生物测量得以一致且无破坏性.
科学领域:
- 生物科学
- 显微镜
- 计算生物学
背景情况:
- 传统的显微镜方法有其局限性.
- 这些包括不一致性,光谱重叠问题以及修复细胞的需要,扰乱实验.
- 通过当前的技术,获取某些生物测量可能是有问题的或不可能的.
研究的目的:
- 为生物样本分析引入一种新的计算机器学习方法.
- 通过透射光显微镜从未标记的样本中预测光标签的能力.
- 克服与显微镜中的传统物理标记方法相关的缺点.
主要方法:
- 开发一种称为"in silico labeling" (ISL) 的计算机器学习方法.
- ISL分析生物样本 (固定或实时) 的传输光图像,以预测光标签.
- 该方法不需要物理光标签或实验性扰动.
主要成果:
- ISL可靠地预测各种光标签,包括核标记,细胞类型 (例如神经) 和细胞状态 (例如细胞死亡).
- 计算方法提供了没有光谱重叠限制的一致预测.
- 通过ISL可以获得传统方法难以或不可能的生物测量结果.
结论:
- 在标签 (ISL) 提供了一个一致的,非破坏性的替代传统的物理标签在显微镜.
- 这种计算方法扩大了从未标记的样本中获取生物数据的可能性.
- 通过实现以前具有挑战性的测量,ISL有可能彻底改变生命科学研究的各个方面.
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