Structural biology meets data science: does anything change?
Structural biology meets data science: does anything change?
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DOI：
10.1016/j.sbi.2018.09.003
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发表时间：
2018-10-01
影响因子：
6.8
通讯作者：
Bourne, Philip E.
中科院分区：
文献类型：
--
作者：
Mura, Cameron;Draizen, Eli J.;Bourne, Philip E.
数据科学是从数字数据的激增中出现的，加上算法、软件和硬件的进步（例如，GPU计算）。结构生物学的创新也受到类似因素的驱动，这促使我们问：这两个领域能否以迄今为止无法预见的方式相互影响？我们认为答案是肯定的。新的生物学知识存在于序列、结构、功能和疾病之间的关系中，所有这些都在进化阶段发挥作用，数据科学使我们能够大规模地阐明这些关系。在这里，我们从数据科学的五个关键支柱来考虑上述问题：获取，工程，分析，可视化和政策，重点是机器学习作为首要的分析方法。
Data science has emerged from the proliferation of digital data, coupled with advances in algorithms, software and hardware (e.g., GPU computing). Innovations in structural biology have been driven by similar factors, spurring us to ask: can these two fields impact one another in deep and hitherto unforeseen ways? We posit that the answer is yes. New biological knowledge lies in the relationships between sequence, structure, function and disease, all of which play out on the stage of evolution, and data science enables us to elucidate these relationships at scale. Here, we consider the above question from the five key pillars of data science: acquisition, engineering, analytics, visualization and policy, with an emphasis on machine learning as the premier analytics approach.