非负矩阵三因子化用于 Multi-omics 数据集中的表示学习,并用于药物重新定位和选择
Letizia Messa1, Carolina Testa1, Stephana Carelli2,3
1Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, 20133 Milan, Italy.
International journal of molecular sciences
|September 14, 2024
概括
我们引入非负矩阵三因素化来整合生物医学数据以用于人工智能驱动的药物设计. 这种方法增强了药物重定向和目标预测,比传统方法显著改进.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算药理学计算药理学
- 人工智能在药物发现中的作用
背景情况:
- 不同质的生物医学数据为药物设计提供了潜力,但需要整合.
- 目前的方法很难有效地将各种数据源用于人工智能分析.
- 开发可靠的数据表示是产生新药候选药物的关键.
研究的目的:
- 为药物设计中的数据集成和表示学习提出非负矩阵三元化 (NMTF).
- 用传统的人工智能方法证明NMTF衍生表示的实用性.
- 在计算药理学中应用和验证NMTF,特别是用于药物重新定位和预测药物协同作用.
主要方法:
- 非负矩阵三因素化 (NMTF) 用于数据集成,融合和表示学习.
- 对异质生物医学数据源 (数据库,本体学,术语) 应用NMTF.
- 使用NMTF学习的表示与既有的人工智能技术.
主要成果:
- NMTF有效地整合和融合各种生物医学数据用于AI分析.
- 在药物设计任务中,NMTF学习的表示方法可以提高传统人工智能方法的性能.
- 验证了新的药物标预测,并证明了预测药物协同作用的有效性.
结论:
- 非负矩阵三因子化是一种灵活而强大的工具,用于生物医学数据集成和表示学习.
- 该方法显著推进了计算药理学,药物重新定位和个性化医学.
- 在各种药物设计和发现挑战中,NMTF具有广泛的适用性.
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