整合基因网络的BERTwalk用于预测基因到通路级别的属性
1School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel.
Bioinformatics advances
|July 14, 2023
概括
BERTwalk是一种新的图形表示学习方法,使用BERT和网络传播集成生物网络. 它在酵母和人类基准中表现优于现有的技术,使得途径推断成为可能.
科学领域:
- 数据科学数据科学数据科学
- 生物信息学是一种生物信息学.
- 网络生物学 网络生物学
背景情况:
- 图形表示学习对于分析生物网络至关重要.
- 以前的方法往往是浅的或使用任意组件.
- 像BIONIC这样的深度学习技术显示出希望,但存在局限性.
研究的目的:
- 开发一个无监督的图形表示学习方案.
- 有效地整合多样化的生物网络.
- 推断路径级别属性及其影响.
主要方法:
- BERTwalk 结合了 BERT 掩盖语言模型与网络传播规范化.
- 将网络数据转换为基于文本的格式,用于集成.
- 应用无监督学习来提取特征.
主要成果:
- 在酵母和人类的基准指标上,BERTwalk的表现优于BIONIC和其他四种方法.
- 该方法成功地整合了不同的生物网络.
- 由BERTwalk生成的特征通知节点,边缘和路径属性.
结论:
- BERTwalk为生物学中的图形表示学习提供了一种强大的新方法.
- 该方法有助于推断功能途径及其影响.
- 伯特步行提供了一个灵活的框架,用于整合性网络分析.
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