结构敏感的变压器和多视图对比学习增强了与药物有关的微生物的预测
Ping Xuan1,2, Rui Wang2, Jing Gu3
1School of Cyberspace Security, Hainan University, Haikou, 570228, China.
BMC bioinformatics
|September 27, 2025
概括
本研究介绍了SMMDA,这是一种通过整合拓和多视图特征来预测微生物药物协会的新型模型. 通过准确识别与药物相关的微生物,SMMDA提高了药物有效性和毒性分析.
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
- 微生物组研究 微生物组研究
背景情况:
- 人类微生物组显著影响药物的疗效和毒性.
- 预测微生物与药物之间的关联有助于理解药物机制.
- 现有的图形学习方法难以完全捕捉生物数据结构和拓信息.
研究的目的:
- 开发一种先进的模型来预测微生物与药物之间的关联.
- 提高在生物图中拓和位置信息的利用率.
- 加强对药物相关微生物的分析,以获得功能性见解.
主要方法:
- 拟议的SMMDA (结构敏感的变压器和多视图对比学习).
- 使用可学习数据增强用于稀疏的药物和微生物特征.
- 利用结构敏感的变压器将药物/微生物拓集成到多视图嵌入式中.
- 实施双重对比学习策略和视图级关注功能集成.
主要成果:
- 在预测与药物相关的微生物方面,SMMDA显著超过现有的最先进的方法.
- 废除研究证实了可学习数据增强,结构敏感变压器和多视图对比学习的有效性.
- 案例研究表明,SMMDA有能力识别潜在的药物候选微生物.
结论:
- SMMDA为预测微生物与药物之间的关联提供了一个强大的框架.
- 该模型有效地整合了各种数据特征,包括拓和语义.
- SMMDA在微生物组与药物相互作用分析和药物开发领域取得了进展.
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