GPO-VAE:模拟可解释的基因扰动反应,利用GRN对齐的参数优化
Seungheun Baek1,2, Soyon Park1, Yan Ting Chok1
1Department of Computer Science and Engineering, Korea University, Seoul, 02841, South Korea.
Bioinformatics (Oxford, England)
|July 15, 2025
概括
我们开发了GPO-VAE,这是一种新型变异自编码器 (VAE),集成基因调节网络 (GRNs) 以可解释地预测细胞对遗传干扰的反应. 这种方法提高了生物AI的解释性,并实现了最先进的性能.
科学领域:
- 计算生物学 计算生物学
- 系统生物学 系统生物学
- 人工智能在生物学中的应用
背景情况:
- 预测细胞对遗传干扰的反应对于生物学理解和治疗至关重要.
- 变化自编码器 (VAE) 显示出潜力,但缺乏生物解释性.
- 基因调控网络 (GRNs) 提供了一条解释生物AI模型的途径.
研究的目的:
- 开发一种可解释的VAE模型,用于预测细胞对遗传干扰的反应.
- 通过整合GRNs,提高生物AI中的深度学习模型的可解释性.
- 在扰乱预测方面实现最先进的性能,同时提供生物学上有意义的解释.
主要方法:
- 拟议的 GPO-VAE,一个包含 GRN 调整的参数优化 VAE 模型.
- 在VAE潜伏空间内明确建模的基因调节网络.
- 优化了模型参数,用于GRN对准的隐性扰动效应的解释性.
主要成果:
- 在预测跨基准数据集的转录反应方面,GPO-VAE实现了最先进的性能.
- 该模型在GRN推理方面表现出强的表现,产生了有意义的网络.
- 定性分析证实了GPO-VAE能够构建符合已知的途径的生物可信的GRNs的能力.
结论:
- GPO-VAE成功地将GRN集成到VAE中,以进行可解释的扰动响应预测.
- 该模型在生物学AI解释性方面取得了重大进展.
- GPO-VAE为了解遗传乱和设计向治疗提供了强大的工具.
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