多域知识图嵌入用于基因疾病关联预测和预测.
Susana Nunes1, Rita T Sousa2, Catia Pesquita2
1LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal. scnunes@ciencias.ulisboa.pt.
Journal of biomedical semantics
|August 14, 2023
概括
通过使用跨多个相互连接的生物医学本体学的知识图嵌入来改进预测基因疾病关联. 这种方法增强了机器学习模型,以更准确地发现基因与疾病的联系.
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.
背景情况:
- 预测基因疾病关联需要整合多种数据源和先进的计算方法.
- 知识图嵌入为机器学习使用本体知识表示基因和疾病提供了一种方式.
- 当前的方法往往忽视了使用多个相互连接的本体学来进行复杂的生物预测的好处.
研究的目的:
- 利用知识图嵌入开发一种用于预测基因疾病关联的新方法.
- 从多个相互连接的本体学中利用丰富的语义表示.
主要方法:
- 利用知识图嵌入在多个本体学上,通过逻辑定义和映射联系在一起.
- 探索了丰富的语义表示,以改善基因疾病的预测.
主要成果:
- 提出的方法显著提高了基因疾病预测的准确性.
- 不同的知识图嵌入技术显示,从语义丰富中获得不同的好处.
结论:
- 跨相互连接的生物医学本体学的知识图嵌入显示了基因疾病预测的前景.
- 这种方法可以扩展到其他需要多视角数据分析的本体学和任务.
- 软件和数据是公开提供进一步研究.
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