从基因组深度神经网络中解释cis-regulatory机制,使用代用模型
Evan E Seitz1, David M McCandlish1, Justin B Kinney1
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
bioRxiv : the preprint server for biology
|November 28, 2023
概括
我们开发了SQUID,这是一个用于解读基因组学深度神经网络 (DNN) 的新框架. SQUID使用可解释的代孕模型来揭示基因组功能预测的基础生物机制.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- 深度神经网络 (DNN) 擅长从DNA序列预测基因组功能.
- 解释这些基因组DNN预测背后的生物机制仍然是一个重大挑战.
研究的目的:
- 引入SQUID,这是一个新的框架,用于提高基因组DNN的可解释性.
- 为了使基因组功能预测的机械理解,从DNNs.
主要方法:
- 在指定序列空间内,SQUID采用代理建模来近似基因组DNA.
- 它使用更简单,机械可解释的模型来表示复杂的DNN行为.
- 该框架解决了功能基因组学数据中的非线性和异构基因组噪声等混因素.
主要成果:
- 基准测试表明,与现有的可解释性方法相比,SQUID的性能优越.
- 在基因组位置上,SQUID 能够识别出更一致的动机.
- 它为单核酸变异效应提供了改进的预测.
- 该框架量化了 cis-regulatory 元素内部和间的表观相互作用.
结论:
- SQUID显著提高了对基因组DNN的机械解释能力.
- 它提供了一种强大的方法,可以从基于序列的基因组模型中发现生物学见解.
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