使用可解释的机器学习来发现酶-基质相互作用景观
Zhongliang Zhou1, Wayland Yeung2, Saber Soleymani1
1School of Computing, University of Georgia, Athens, GA 30602, United States.
Bioinformatics (Oxford, England)
|January 20, 2024
概括
我们开发了一个可解释的AI模型来预测酶-基质关系,提高酸化研究的准确性和可解释性. 这个工具通过分析酶特异性来帮助理解细胞信号传递.
科学领域:
- 生物信息学是一种生物信息学.
- 细胞信号传输 细胞信号传输
- 蛋白质组学是指蛋白质组学
背景情况:
- 酸化是一种关键的翻译后修饰,调节细胞过程.
- 预测酶-基质关系对于理解细胞信号来说至关重要.
- 对于激酶基质预测的现有深度学习模型缺乏解释性,并以偏见的数据集进行训练.
研究的目的:
- 开发一种可解释的变压器模型,用于预测酶-相互作用.
- 为了利用类图书馆数据集对大量的氨酸/氨酸激酶进行训练.
- 通过可解释的人工智能方法,提供对模型决策过程的见解.
主要方法:
- 开发了一种可解释的变压器模型,仅在初级序列上进行训练.
- 利用多任务学习进行广泛的酶-相互作用预测.
- 采用夏普利添加剂扩张 (SHAP) 进行残留水平分析.
主要成果:
- 在激酶-相互作用预测方面取得了最先进的性能.
- 启用了未包括在训练数据集中的激酶的预测.
- 确定了关键的特异性决定残留物,并揭示了模型的基质预测策略.
结论:
- 开发的模型提供了一个高度准确和可解释的方法来预测酶-基质关联.
- 该模型能够将其推广到未见的激酶,并提供机械学的洞察力,从而推动了细胞信号传递领域的发展.
- 为更广泛的可访问性和应用提供了一个Web界面和资源.
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