稀有的自编码器在蛋白质语言模型表示中发现了生物可解释的特征
Onkar Gujral1,2, Mihir Bafna2, Eric Alm3,4
1Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139.
概括
稀疏的自编码器可以从蛋白质语言模型 (PLM) 中提取可解释的特征,而无需监督. 这些特征揭示了生物学见解,提高了AI在生命科学中的解释性和信任.
科学领域:
- 计算生物学 计算生物学
- 生命科学中的人工智能
- 生物信息学是一种生物信息学.
背景情况:
- 蛋白质语言模型 (PLM) 具有先进的生物预测,但存在"黑子"性质,限制了透明度和可解释性.
- 解释PLM学习的特征对于人类-AI协作和理解生物机制至关重要.
- 现有的特征解释方法通常需要监督,这可能是劳动密集型的,可能无法捕获所有相关信息.
研究的目的:
- 开发一种无监督的方法,从蛋白质水平和氨基酸水平的PLM表示中提取可解释的特征.
- 评估提取的稀疏特征的生物相关性和解释性.
- 提高PLM在生物应用中的安全性,信任性和可解释性.
主要方法:
- 利用从自然语言处理中调整的稀疏自编码器 (SAEs) 和转码器,从ESM2 PLM中提取特征.
- 在完全无监督的情况下进行特征提取,而不依赖外部的生物标签或探针.
- 利用Anthropic的Claude AI来自动解释提取的稀疏特征.
主要成果:
- SAE成功地从ESM2.2的蛋白质水平和氨基酸水平表示中提取了可解释的稀疏特征.
- 许多提取的稀疏特征显示了与基因本体学 (GO) 术语在所有层次层次上的强烈关联.
- 自动解释确定了与特定蛋白质家族 (例如,NAD激酶,PTH) 相应的特征,功能 (例如,甲基转移酶活性) 和感官感知.
结论:
- SAE提供了一种强大的无监督方法,可以在PLM表示中解开生物相关信息.
- 提取的稀疏特征比原始的PLM神经元更容易解释,有助于发现生物洞察力.
- 这项工作显著提高了PLM的可解释性和可信度,为更深入的生物学理解铺平了道路.
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