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在疾病诊断中的图像分类多omics方法

Yan Lin¹, Shu Chen², Jinshan Che³

¹Department of Critical Care Medicine, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, NHC Key Laboratory of Cancer Metabolism, Fuzhou, China.

Frontiers in cellular and infection microbiology

|December 22, 2025

概括

整合多omics数据通过分析宿主-微生物相互作用提供了强大的疾病诊断. 我们的计算模型为精确诊断提供可解释,可扩展的见解.

科学领域:

计算生物学是一种计算生物学.
微生物组研究的研究.
系统生物学系统生物学

背景情况:

疾病诊断往往忽视了宿主微生物相互作用和系统因素.
单模态方法 (例如,遗传病理学,基因组学) 在可扩展性,概括性和处理复杂的生物数据方面存在局限性.
需要强大的计算模型来分析高维,异质的omics数据.

研究的目的:

开发可解释,可扩展和生物稳定的疾病诊断计算模型.
整合多omics数据 (基因组,转录组,蛋白质组,代谢组) 以全面了解疾病机制.
克服传统单模诊断方法的局限性.

主要方法:

跨基因组,转录基因组,蛋白质基因组和代谢基因组层的多omics数据集成.
开发可解释和可扩展的计算模型.
专注于从复杂的生物数据集中提取具有临床意义的诊断见解.

主要成果:

这项研究表明了多omics集成的潜力,用于增强疾病诊断.
拟议的方法通过捕获各种生物信号,提供了对疾病机制的更全面的理解.
这些模型旨在提供基于生物学和临床可行的诊断见解.

结论:

多omics数据集成对于推进计算生物学和疾病诊断至关重要.
可解释和可扩展的计算模型对于充分利用复杂生物数据集的全部潜力至关重要.
这种方法为准确医学下一代诊断工具铺平了道路.

关键词:

疾病微生物学疾病微生物学平衡推理策略平衡推理策略主体 - 病原体动态图像的分类图像的分类.多主题整合多主题整合.

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