Bayesian networks in modeling leucocyte interplay following brain irradiation: A comprehensive framework

IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
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引用次数: 0

Abstract

Background and objective

Understanding the intricate interactions among leucocyte subpopulations following radiotherapy is crucial for advancing cancer research and immunology. Recently, interest in recent radiotherapy modalities, such as protons, has increased. Herein, we present a framework utilizing Bayesian networks to uncover these complex relationships via an illustrative example of brain irradiation in rodents.

Methods

We utilized data from 96 healthy C57BL/6 adult mice subjected to either X-ray or proton brain irradiation. Leucocyte subpopulations in the blood collected 12 h after the final irradiated fraction were quantified. We employed Bayesian networks to detect causal interplay between physiological parameters, radiation variables and circulating leucocytes. The causal structure was learned via the use of the Bayesian information criterion as a scored criterion. Parameter estimation was performed to quantify the strength of the identified causal relationships. Cross-validation was used to validate our Bayesian network model's performance.

Results

In the X-ray model, we discovered previously undisclosed interactions between NK-cells and neutrophils, and between monocytes and T-CD4+ cells. The proton model revealed an interplay involving T-CD4+ cells and neutrophils. Both X-rays and protons led to heightened interactions between T-CD8+ cells and B cells, indicating their significant role in orchestrating immune responses. Additionally, the proton model displayed strengthened interactions between T-CD4+ and T-CD8+ cells, emphasizing a dynamic and coordinated immune response post-irradiation. Cross-validation results demonstrated the robustness of the Bayesian network model in explaining data uncertainty.

Conclusion

The use of Bayesian networks as tools for causal structure discovery has revealed novel insights into the dynamics of immune responses to radiation exposure.

贝叶斯网络在脑部照射后白细胞相互作用建模中的应用:综合框架
背景和目的 了解放疗后白细胞亚群之间错综复杂的相互作用对于推动癌症研究和免疫学的发展至关重要。最近,人们对质子等最新放疗方式的兴趣与日俱增。我们利用 96 只健康的 C57BL/6 成年小鼠接受 X 射线或质子脑辐照后的数据,建立了一个贝叶斯网络框架,以揭示这些复杂的关系。我们对最终辐照后 12 小时收集的血液中的白细胞亚群进行了量化。我们采用贝叶斯网络来检测生理参数、辐射变量和循环白细胞之间的因果关系。通过使用贝叶斯信息标准作为评分标准来学习因果结构。通过参数估计来量化已识别因果关系的强度。在 X 射线模型中,我们发现了 NK 细胞与中性粒细胞之间以及单核细胞与 T-CD4+ 细胞之间以前未曾披露的相互作用。质子模型揭示了 T-CD4+ 细胞与中性粒细胞之间的相互作用。X 射线和质子都增强了 T-CD8+ 细胞与 B 细胞之间的相互作用,表明它们在协调免疫反应中发挥着重要作用。此外,质子模型加强了 T-CD4+ 和 T-CD8+ 细胞之间的相互作用,强调了辐照后动态协调的免疫反应。交叉验证结果证明了贝叶斯网络模型在解释数据不确定性方面的稳健性。
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来源期刊
Computer methods and programs in biomedicine
Computer methods and programs in biomedicine 工程技术-工程:生物医学
CiteScore
12.30
自引率
6.60%
发文量
601
审稿时长
135 days
期刊介绍: To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine. Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.
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