对变压器模型的内部运作进行洞察,以预测蛋白质功能
Markus Wenzel1, Erik Grüner1, Nils Strodthoff2
1Department of Artificial Intelligence, Fraunhofer Institute for Telecommunications, Heinrich-Hertz-Institut, HHI, Einsteinufer 37, 10587 Berlin, Germany.
Bioinformatics (Oxford, England)
|January 20, 2024
概括
可解释的人工智能方法,如集成梯度,现在检查神经网络内部工作的蛋白质功能预测. 这揭示了变压器模型中的关键氨基酸和生物学相关的序列模式.
科学领域:
- 计算生物学 计算生物学
- 人工智能的人工智能
- 生物化学 生物化学
背景情况:
- 神经网络是用于蛋白质功能预测的强大工具.
- 了解这些模型的决策过程对于信任和发展至关重要.
- 现有的可解释AI (XAI) 方法提供了洞察力,但可能无法完全捕捉复杂的模型架构.
研究的目的:
- 扩展可解释的人工智能 (XAI) 方法,以深入了解用于蛋白质功能预测的神经网络.
- 为了能够检查变压器模型中的潜在表示.
- 为了提高预测基因本体学术语和酶委员会号码的模型的可解释性.
主要方法:
- 扩展了集成梯度XAI方法.
- 将扩展方法应用于用于蛋白质功能预测的微调变压器模型.
- 在嵌入层和内部变压器层中分析了归属图.
主要成果:
- 确定了特定的氨基酸,这些氨基酸对于蛋白质序列中的变压器注意力至关重要.
- 证明这些重要的序列区域符合生物和化学预期.
- 发现的变压器头与归因地图和已知的蛋白质注释 (例如,活性位点,跨膜区域) 之间具有统计学意义的相关性.
结论:
- 扩展的XAI方法为蛋白质功能预测模型提供了宝贵的见解.
- 这种方法增强了对变压器如何处理蛋白质序列信息的理解.
- 这些发现支持这些人工智能模型学习的特征的生物学相关性.
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