利用基于 4D 数据独立采集的蛋白质组分析和机器学习来揭示压力水平的潜在生物标志物。

IF 0.9 4区 生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Dehua Chen, Yongsheng Yang, Dongdong Shi, Zhenhua Zhang, Mei Wang, Qiao Pan, Jianwen Su, Zhen Wang
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引用次数: 0

摘要

研究表明,长期承受压力的人患心理应激障碍的风险可能更高。目前,心理压力主要由专业医生使用评分量表进行评估,这可能容易产生主观偏见和量表的局限性。因此,探索更客观、准确、高效的生物标志物来评估个体的心理压力水平势在必行。在本研究中,我们利用四维数据独立采集(4D-DIA)蛋白质组学进行定量蛋白质分析,然后采用支持向量机(SVM)结合SHAP解释算法来识别心理压力水平的潜在生物标志物。随后,通过机器学习分类和从知识图谱中获得的大量先验知识实现了生物标记物的验证。我们使用两批数据对生物标记物进行了交叉验证,结果显示,甘油醛-3-磷酸脱氢酶和纤连蛋白的组合产生的平均曲线下面积(AUC)为 92%,平均准确率为 86%,平均 F1 得分为 79%,平均灵敏度为 83%。因此,这种组合可能是检测压力水平以预防心理应激障碍的一种潜在方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The use of 4D data-independent acquisition-based proteomic analysis and machine learning to reveal potential biomarkers for stress levels.

Research suggests that individuals who experience prolonged exposure to stress may be at higher risk for developing psychological stress disorders. Currently, psychological stress is primarily evaluated by professional physicians using rating scales, which may be prone to subjective biases and limitations of the scales. Therefore, it is imperative to explore more objective, accurate, and efficient biomarkers for evaluating the level of psychological stress in an individual. In this study, we utilized 4D data-independent acquisition (4D-DIA) proteomics for quantitative protein analysis, and then employed support vector machine (SVM) combined with SHAP interpretation algorithm to identify potential biomarkers for psychological stress levels. Biomarkers validation was subsequently achieved through machine learning classification and a substantial amount of a priori knowledge derived from the knowledge graph. We performed cross-validation of the biomarkers using two batches of data, and the results showed that the combination of Glyceraldehyde-3-phosphate dehydrogenase and Fibronectin yielded an average area under the curve (AUC) of 92%, an average accuracy of 86%, an average F1 score of 79%, and an average sensitivity of 83%. Therefore, this combination may represent a potential approach for detecting stress levels to prevent psychological stress disorders.

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来源期刊
Journal of Bioinformatics and Computational Biology
Journal of Bioinformatics and Computational Biology MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
2.10
自引率
0.00%
发文量
57
期刊介绍: The Journal of Bioinformatics and Computational Biology aims to publish high quality, original research articles, expository tutorial papers and review papers as well as short, critical comments on technical issues associated with the analysis of cellular information. The research papers will be technical presentations of new assertions, discoveries and tools, intended for a narrower specialist community. The tutorials, reviews and critical commentary will be targeted at a broader readership of biologists who are interested in using computers but are not knowledgeable about scientific computing, and equally, computer scientists who have an interest in biology but are not familiar with current thrusts nor the language of biology. Such carefully chosen tutorials and articles should greatly accelerate the rate of entry of these new creative scientists into the field.
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