空间:串蛋白作为互补的嵌入物
Dewei Hu1, Damian Szklarczyk2,3, Christian von Mering2,3
1Novo Nordisk Foundation Center for Protein Research, Department of Cellular and Molecular Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark.
Bioinformatics (Oxford, England)
|September 9, 2025
概括
我们使用蛋白质网络和ortology开发了跨物种的蛋白质嵌入,改进了蛋白质功能和本地化预测. 这些网络嵌入补充序列数据,以增强真核生物中的机器学习.
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
- 机器学习 机器学习
背景情况:
- 代表性学习推进了基于序列的蛋白质功能和本地化预测.
- 蛋白质网络提供互补的数据,但也带来了跨物种机器学习的挑战.
研究的目的:
- 从1322个真核生物的蛋白质网络中生成跨物种的蛋白质嵌入.
- 为了调整物种特定的网络嵌入,使用正统学进行跨物种比较.
- 评估这些对齐的嵌入式对蛋白质功能和局部化预测的实用性.
主要方法:
- 利用STRING数据库进行蛋白质网络和 ортология关系.
- 创建了特定物种的网络嵌入,然后使用ortology对准它们.
- 验证了对齐的网络嵌入,用于预测任务的序列嵌入.
主要成果:
- 对齐的网络嵌入保持了物种之间的质量和一致性.
- 对齐网络嵌入是对序列嵌入的补充.
- 使用对齐的网络和序列嵌入,提高了亚细胞局部化和蛋白质功能的预测准确性.
结论:
- 跨物种蛋白质网络的嵌入增强了预测模型的性能.
- 开发的方法提供了一种强大的方法来整合跨物种的网络信息.
- 这种方法接近最先进的深度学习性能,用于蛋白质预测任务.
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