Imaging, Visualization, and Computation in Developmental Biology

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Annual Review of Biomedical Data Science Pub Date : 2019-07-22 DOI:10.1146/ANNUREV-BIODATASCI-072018-021305

F. Cutrale, S. Fraser, Le A. Trinh

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引用次数: 13

Abstract

Embryonic development is highly complex and dynamic, requiring the coordination of numerous molecular and cellular events at precise times and places. Advances in imaging technology have made it possible to follow developmental processes at cellular, tissue, and organ levels over time as they take place in the intact embryo. Parallel innovations of in vivo probes permit imaging to report on molecular, physiological, and anatomical events of embryogenesis, but the resulting multidimensional data sets pose significant challenges for extracting knowledge. In this review, we discuss recent and emerging advances in imaging technologies, in vivo labeling, and data processing that offer the greatest potential for jointly deciphering the intricate cellular dynamics and the underlying molecular mechanisms. Our discussion of the emerging area of “image-omics” highlights both the challenges of data analysis and the promise of more fully embracing computation and data science for rapidly advancing our understanding of biology.

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发育生物学中的成像、可视化和计算

胚胎发育是高度复杂和动态的，需要在精确的时间和地点协调许多分子和细胞事件。成像技术的进步使得在细胞、组织和器官水平上跟踪完整胚胎的发育过程成为可能。体内探针的平行创新允许成像报告胚胎发生的分子、生理和解剖事件，但由此产生的多维数据集对提取知识构成了重大挑战。在这篇综述中，我们讨论了成像技术、体内标记和数据处理方面的最新进展，这些进展为共同破译复杂的细胞动力学和潜在的分子机制提供了最大的潜力。我们对“图像组学”这一新兴领域的讨论强调了数据分析的挑战，以及更全面地拥抱计算和数据科学以快速推进我们对生物学的理解的承诺。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Annual Review of Biomedical Data Science

CiteScore

11.10

自引率

1.70%

发文量

期刊介绍： The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.