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.

The Coming of Age for Big Data in Systems Radiobiology, an Engineering Perspective

Mastroberardino, Pier G;
2020

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11697/153603
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