生物
可见的
步伐
国家(计算机科学)
概念框架
数据科学
透视图(图形)
领域(数学)
认识论
人工智能
物理
计算机科学
量子力学
数学
哲学
纯数学
算法
天文
作者
Susanne M. Rafelski,Julie A. Theriot
出处
期刊:Cell
[Elsevier]
日期:2024-05-01
卷期号:187 (11): 2633-2651
被引量:9
标识
DOI:10.1016/j.cell.2024.04.035
摘要
Cell states were traditionally defined by how they looked, where they were located, and what functions they performed. In this post-genomic era, the field is largely focused on a molecular view of cell state. Moving forward, we anticipate that the observables used to define cell states will evolve again as single-cell imaging and analytics are advancing at a breakneck pace via the collection of large-scale, systematic cell image datasets and the application of quantitative image-based data science methods. This is, therefore, a key moment in the arc of cell biological research to develop approaches that integrate the spatiotemporal observables of the physical structure and organization of the cell with molecular observables toward the concept of a holistic cell state. In this perspective, we propose a conceptual framework for holistic cell states and state transitions that is data-driven, practical, and useful to enable integrative analyses and modeling across many data types.
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