多数据集集成和剩余连接 通过使用深度学习改进从转录组的蛋白质组预测
Caleb W Cranney1,2,3, Jesse G Meyer1,2,3
1Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles CA 90048.
bioRxiv : the preprint server for biology
|July 19, 2024
概括
深度学习模型从转录组学改进了蛋白质组预测. 一个关键的发现是,在记住输入数据的神经架构搜索 (NAS) 模型中,残余连接的好处.
科学领域:
- 生物化学 生物化学
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 蛋白质组和转录组数据经常显示不良相关性.
- 从基因表达 (转录组学) 中预测蛋白质量是具有挑战性的.
- 了解转录蛋白关系在生物研究中至关重要.
研究的目的:
- 为了提高从转录基因数据中预测蛋白质组数量的准确性.
- 调查深度学习架构对此预测任务的影响.
- 为了确定蛋白质预测的功能性重要转录.
主要方法:
- 利用了来自临床蛋白质学瘤分析联盟 (CPTAC) 的公开数据.
- 采用通过神经架构搜索 (NAS) 开发的深度学习模型.
- 应用模型解释技术 (SHAP) 来分析转录的重要性.
主要成果:
- 深度学习模型,特别是那些具有残余连接的模型,显著提高了蛋白质组预测的准确性.
- 发现剩余连接对于在网络中保留输入信息至关重要.
- SHAP分析确定了特定的转录组,这些组对于准确的蛋白质水平预测至关重要.
结论:
- 神经架构搜索驱动的深度学习提供了一种强大的方法,用于从转录组中预测蛋白质组.
- 建筑选择,就像剩余连接一样,极大地影响了模型的性能.
- 模型可解释性方法可以揭示对基因-蛋白质关系的生物学见解.
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