一种可解释的图形神经网络方法,有效地整合多种经验与先前的知识,以识别来自交互的生物领域的生物标志物
Rohit K Tripathy1, Zachary Frohock1, Hong Wang1
1The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
bioRxiv : the preprint server for biology
|September 10, 2024
概括
将多学科数据与生物知识图表集成,可以改善疾病预测. 我们的框架使用图形神经网络和设置变压器来识别阿尔茨海默病的生物标志物.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 系统生物学 系统生物学
背景情况:
- 越来越多的多学科数据需要先进的集成方法.
- 生物学的先验知识,如路径和相互作用,对于稳健的分析至关重要.
- 现有的方法往往难以有效地将各种omics数据与网络信息结合起来.
研究的目的:
- 开发一个监督的框架,将多学科数据与生物知识图表集成在一起.
- 提高疾病的预测建模,并识别分子标记物.
- 将生物标志物发现和关系提取的可解释性纳入其中.
主要方法:
- 利用图形神经网络 (GNN) 来建模高维的奥米特征关系.
- 雇佣的集成变压器用于集成低维的奥米克特征表示.
- 整合了可解释性技术,以识别关键生物标志物和生物相互作用.
主要成果:
- 证明了对阿尔茨海默病 (AD) 状态的预测准确度的提高.
- 成功地将转录组学和蛋白质组学数据与AD特定网络的先验数据集成.
- 突出功能性AD生物标志物及其关系.
结论:
- 拟议的框架有效地整合了多学科数据和生物知识,以改善疾病预测.
- 这种方法有助于发现重要的生物标志物和相互作用网络.
- 这种方法为推进精准医学和理解AD等复杂疾病提供了强大的工具.
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