Big data in radiation biology and epidemiology; an overview of the historical and contemporary landscape of data and biomaterial archives

Schofield, Paul N.; Kulka, Ulrike; Tapio, Soile; Grosche, Bernd

doi:10.1080/09553002.2019.1589026

Cited by 21 publications

(17 citation statements)

References 117 publications

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“…Among all applications of DL, the medical material might not be anything special or significant due to its small data quantity. More likely, the medical material becomes an extension of biomaterials [95,96] , biocompatible studies [97] , or biomedical oriented study sourced from general datasets. Such a situation is not likely to weaken the study of medical material, rather push the medical material field to be fused with other subjects and form some interdisciplinary ideas.…”

Section: Deep Learning and Medical Materialsmentioning

confidence: 99%

Deep Learning for Medical Materials: Review and Perspective

Qi¹,

Liu²

2021

ES Mater. Manuf.

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Deep learning and similar computational research approaches have cooperated with materials research for years. Especially in the last few years, with the fast evolution of machine learning and deep learning algorithms, a novel branch for material research is presented to be recognized, learned, practiced, adapted and perfected. Different conventional computational modeling methods, the deep learning approach assists material science and engineering from the aspect of data processing and analysis, rather than simulate the reality. However, mistakes always exist in all less explored concepts and developments. Here, in order to offer a better instruction of deep learning for materials scientists, several recent works on deep learning based material research were reviewed. The application of deep learning in material research is introduced and discussed. As an example of both general material and advanced material, medical material was discussed for the impact and future of deep learning methods. At last, we provided several insights on future work for both deep learning scientists, data scientists and materials scientists.

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Section: Deep Learning and Medical Materialsmentioning

confidence: 99%

Deep Learning for Medical Materials: Review and Perspective

Qi¹,

Liu²

2021

ES Mater. Manuf.

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“…Also, the limited sensitivity and specificity of the assays imply a source of misclassification (as discussed in Gomolka et al 2019 in this Special Issue). Recently, Schofield and colleagues discussed the issue of big data in radiation biology including biomaterial archives (Schofield et al 2019).…”

Section: Collection Of Biological Materialsmentioning

confidence: 99%

Clinical and epidemiological observations on individual radiation sensitivity and susceptibility

Seibold

Auvinen

Averbeck

et al. 2019

International Journal of Radiation Biology

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Purpose: To summarize existing knowledge and to understand individual response to radiation exposure, the MELODI Association together with CONCERT European Joint Programme has organized a workshop in March 2018 on radiation sensitivity and susceptibility. Methods: The workshop reviewed the current evidence on this matter, to inform the MELODI Strategic Research Agenda (SRA), to determine social and scientific needs and to come up with recommendations for suitable and feasible future research initiatives to be taken for the benefit of an improved medical diagnosis and treatment as well as for radiation protection. Results: The present paper gives an overview of the current evidence in this field, including potential effect modifiers such as age, gender, genetic profile, and health status of the exposed population, based on clinical and epidemiological observations. Conclusion: The authors conclude with the following recommendations for the way forward in radiation research: (a) there is need for large (prospective) cohort studies; (b) build upon existing radiation research cohorts; (c) use data from well-defined cohorts with good exposure assessment and biological material already collected; (d) focus on study quality with standardized data collection and reporting; (e) improve statistical analysis; (f) cooperation between radiobiology and epidemiology; and (g) take consequences of radiosensitivity and radiosusceptibility into account.

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“…Mothersill and Seymour (2019) consider how the old ideas in the studies of low dose effects evolved, leading to apparently abrupt paradigm shifts. Schofield et al (2019) review the creation, sustainability and reuse of legacy data, and discuss the importance of open data and biomaterial archives for contemporary radiobiological sciences, radioecology and epidemiology. Ozasa et al (2019) review cancer and noncancer effects in Japanese atomic bomb survivors and also in their F1 offspring.…”

Section: Editorialmentioning

confidence: 99%