一个可扩展的工具,用于分析人类的基因组变体,使用知识图和图形机器学习
Shivika Prasanna1, Ajay Kumar1, Deepthi Rao2
1Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, United States.
Frontiers in big data
|February 5, 2025
概括
VariantKG是一个新的工具,它使用知识图 (KGs) 和图形机器学习 (GML) 来分析人类基因组变异. 这种方法有助于通过有效地建模遗传数据来理解像COVID-19这样的复杂疾病.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 高通量基因组测序的进步使得临床实践和研究中的大规模基因组数据分析成为可能.
- 基因组变异分析对于理解复杂疾病风险因素至关重要,包括癌症和COVID-19.
- 知识图 (KGs) 和图形机器学习 (GML) 为建模和分析复杂的基因组数据集提供了强大的方法.
研究的目的:
- 介绍VariantKG,这是一个可扩展的工具,用于使用KG和GML分析人类基因组变异.
- 通过使用来自COVID-19患者的公开可用的基因组测序数据来证明VariantKG的应用.
- 为KG构建,模型推理和基因组变异的解释提供一个直观的平台.
主要方法:
- 变异KG提取并注释变异级遗传信息,使用资源描述框架 (RDF) 将其转换为KG.
- 该KG增强了患者元数据,并存储在可扩展的图形数据库中,以进行高效的查询.
- 包括节点分类在内的GML任务使用KG的子集上的Deep Graph Library (DGL) 进行.
主要成果:
- 使用1,508个基因组序列的KG构造导致了40亿个RDF声明.
- 节点分类任务在500个序列的子集上使用GML技术,如GraphSAGE,GCN和Graph Transformer进行了评估.
- 变种KG展示了高效的KG构建,可扩展的数据处理和有效的GML推断.
结论:
- VariantKG提供了一个可扩展和可访问的解决方案,用于通过KG和GML分析人类基因组变异.
- 该工具有助于更深入地了解导致复杂疾病的遗传因素.
- 变种KG降低了研究人员和临床医生使用大规模基因组数据和GML工作的进入障碍.
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