DA-SRN:基于对复杂疾病的样本网络优化的Omics数据分析
Benzhe Su1, Xiaoxiao Wang1, Yang Ouyang2
1School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, China.
Computers in biology and medicine
|July 16, 2023
概括
一种新的方法,DA-SRN,通过分析患者相似性网络,有效地识别生物标志物并预测复杂的OMIC数据中的疾病类别. 这种方法对推进疾病诊断和理解有希望.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 代谢学 代谢学 代谢学
- 文字转录学 (Transcriptomics) 是一个学科.
背景情况:
- 准确的生物标志物识别和样本分类仍然是复杂疾病研究中的重大挑战.
- 患者相似性网络 (PSN) 分析为分析高维的奥米克数据提供了强大的框架.
- PSN 的结构性质可以表明特征空间的分辨力.
研究的目的:
- 引入一种新的数据分析方法,DA-SRN (数据分析-样本参考网络),用于生物标志物发现和样本分类.
- 优化网络结构并使用遗传算法识别信息特征.
- 利用图形神经网络和样本参考网络进行准确的样本标签.
主要方法:
- 开发了DA-SRN,集成了用于特征选择和网络优化的遗传算法.
- 采用图形神经网络来根据优化参考网络和选定的特征进行样本分类.
- 使用基因组学,代谢学和转录学数据集对九种现有方法进行了DA-SRN验证.
主要成果:
- 与现有方法相比,DA-SRN在包括AUROC和AUPRC在内的多个指标上表现出卓越的表现.
- 使用代谢学数据确定了与2型糖尿病 (T2D) 相关的关键代谢物.
- 途径分析证实了已识别的代谢物与T2D病变发生的相关性.
结论:
- 通过分析样本间的关系,DA-SRN有效地从复杂的奥米克数据中提取有价值的生物学见解.
- 该方法显示了生物标志物识别和复杂疾病中的样本歧视的巨大潜力.
- DA-SRN促进了基于网络的准确医学方法的应用.
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