知识图嵌入用于描述转录因子及其目标基因之间的相互作用
Yang-Han Wu1, Yu-An Huang2, Jian-Qiang Li1
1College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guang-dong, China.
PLoS computational biology
|June 20, 2023
概括
本研究介绍了KGE-TGI,这是一种基于图形的新型模型,用于仅使用拓信息来预测基因相互作用及其类型. 该方法实现了最先进的性能,推进了基因调节网络研究.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 系统生物学 系统生物学
背景情况:
- 基因调节网络在人类生物学中至关重要,但仍然复杂.
- 许多已知的转录因子-标基因相互作用缺乏确定的类型.
- 现有的计算方法通常需要基因表达数据,从而限制了它们的范围.
研究的目的:
- 开发一种新的计算模型,用于预测转录因子-向基因相互作用及其类型.
- 仅利用拓信息进行预测,克服现有方法的局限性.
- 建立基准数据集并评估拟议模型的性能.
主要方法:
- 提出KGE-TGI,一种基于图表的预测模型,利用在构造的知识图上进行多任务学习.
- 作为一个多标签分类问题,在异质图上制定了交互类型预测.
- 综合链接预测与链接类型分类.
主要成果:
- 在5倍交叉验证中,在链接预测方面达到0.9654的平均AUC值,在链接类型分类方面达到0.9339的平均AUC值.
- 证明了结合知识信息可以显著提高预测准确性.
- 与现有方法相比,展示了最先进的性能.
结论:
- 该KGE-TGI模型有效地预测基因相互作用和类型使用拓信息.
- 知识图集成提高了基因调节网络分析中的预测准确性.
- 这种方法为理解复杂的生物相互作用提供了有价值的工具.
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