使用机器学习算法识别与昼夜节律相关的生物标志物并开发克罗恩病的诊断模型。

IF 1.7 4区医学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2025-01-21 DOI:10.1080/10255842.2025.2453922

Zhijing Zhao, Xia Chen, Qian Xiang, Liu Liu, Xiaohua Li, Boyun Qiu

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引用次数: 0

摘要

全球克罗恩病（CD）发病率的上升加剧了诊断挑战。本研究使用GEO数据库的数据集确定了CD的昼夜节律相关生物标志物。差异表达基因进行加权基因共表达网络分析，从GeneCards数据中提取49个枢纽基因。使用机器学习算法构建诊断模型，并使用先进的回归技术预测生物治疗疗效。单细胞测序显示干细胞、免疫细胞和内皮细胞中基因高表达，证实了CD患者和对照组之间的显著差异。这些发现表明，昼夜节律相关基因是有希望诊断CD的生物标志物。

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Identification of circadian rhythm-related biomarkers and development of diagnostic models for Crohn's disease using machine learning algorithms.

The global rise in Crohn's Disease (CD) incidence has intensified diagnostic challenges. This study identified circadian rhythm-related biomarkers for CD using datasets from the GEO database. Differentially expressed genes underwent Weighted Gene Co-Expression Network Analysis, with 49 hub genes intersected from GeneCards data. Diagnostic models were constructed using machine learning algorithms, and biologic therapy efficacy was predicted with advanced regression techniques. Single-cell sequencing showed high gene expression in stem cells, immune, and endothelial cells, with validation confirming significant differences between CD patients and controls. These findings suggest circadian rhythm-related genes as promising diagnostic biomarkers for CD.

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来源期刊

Computer Methods in Biomechanics and Biomedical Engineering 工程技术-工程：生物医学

CiteScore

4.10

自引率

6.20%

发文量

179

审稿时长

4-8 weeks

期刊介绍： The primary aims of Computer Methods in Biomechanics and Biomedical Engineering are to provide a means of communicating the advances being made in the areas of biomechanics and biomedical engineering and to stimulate interest in the continually emerging computer based technologies which are being applied in these multidisciplinary subjects. Computer Methods in Biomechanics and Biomedical Engineering will also provide a focus for the importance of integrating the disciplines of engineering with medical technology and clinical expertise. Such integration will have a major impact on health care in the future.