As high-throughput approaches in biological and biomedical research are transforming the life sciences into information-driven disciplines, modern analytics platforms for big data have started to address the needs for efficient and systematic data analysis and interpretation. We observe that radiobiology is following this general trend, with -omics information providing unparalleled depth into the biomolecular mechanisms of radiation response—defined as systems radiobiology. We outline the design of computational frameworks and discuss the analysis of big data in low-dose ionizing radiation (LDIR) responses of the mammalian brain. Following successful examples and best practices of approaches for the analysis of big data in life sciences and health care, we present the needs and requirements for radiation research. Our goal is to raise awareness for the radiobiology community about the new technological possibilities that can capture complex information and execute data analytics on a large scale. The production of large data sets from genome-wide experiments (quantity) and the complexity of radiation research with multidimensional experimental designs (quality) will necessitate the adoption of latest information technologies. The main objective was to translate research results into applied clinical and epidemiological practice and understand the responses of biological tissues to LDIR to define new radiation protection policies. We envisage a future where multidisciplinary teams include data scientists, artificial intelligence experts, DevOps engineers, and of course radiation experts to fulfill the augmented needs of the radiobiology community, accelerate research, and devise new strategies.

中文翻译：

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系统放射生物学大数据时代的到来，工程视角

随着生物和生物医学研究中的高通量方法正在将生命科学转变为信息驱动的学科，大数据的现代分析平台已经开始满足对高效和系统的数据分析和解释的需求。我们观察到放射生物学正在遵循这一普遍趋势，组学信息提供了对辐射反应生物分子机制的无与伦比的深度——定义为系统放射生物学。我们概述了计算框架的设计，并讨论了哺乳动物大脑低剂量电离辐射 (LDIR) 响应中的大数据分析。遵循生命科学和医疗保健大数据分析方法的成功案例和最佳实践，我们提出了辐射研究的需求和要求。我们的目标是提高放射生物学界对可以捕获复杂信息并大规模执行数据分析的新技术可能性的认识。从全基因组实验（数量）和辐射研究的复杂性和多维实验设计（质量）产生的大数据集将需要采用最新的信息技术。主要目标是将研究结果转化为应用临床和流行病学实践，并了解生物组织对 LDIR 的反应，以定义新的辐射防护政策。我们设想未来多学科团队包括数据科学家、人工智能专家、DevOps 工程师，当然还有辐射专家，以满足放射生物学界的增强需求，