解释深度神经网络,用于预测翻译速率
Frederick Korbel1, Ekaterina Eroshok1,2, Uwe Ohler3,4,5
1Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Hannoversche Straße 28, Berlin, 10115, Germany.
BMC genomics
|November 10, 2024
概括
了解mRNA 5'未翻译区域 (5'UTR) 是翻译控制的关键. 我们的研究解读了影响翻译的序列元素,改进了基因调节模型和疾病洞察力.
科学领域:
- 分子生物学分子生物学
- 计算生物学 计算生物学
- 遗传学 是一个遗传学.
背景情况:
- mRNA的5'未翻译区域 (5'UTR) 关键调节翻译启动速率.
- 卷积神经网络 (CNN) 模型可以量化5'UTR序列和翻译水平,但生物驱动因素尚不清楚.
- 识别翻译输出的序列决定因素对于理解5'UTR调节至关重要.
研究的目的:
- 解释CNN模型并发现由5'UTRs调节翻译的生物特征.
- 为了比较训练在合成与自然5'UTR数据上的模型的预测能力.
- 通过整合多样化的序列数据,开发一个改进的人类翻译监管模型.
主要方法:
- 在合成和人类5'UTR记者数据上训练的CNN中应用模型解释技术.
- 分析了监管序列元素,如启动上下文和上游开放阅读框架 (uORF).
- 通过结合合成和人类5'UTR报告员数据开发了OptMRL模型.
主要成果:
- 揭示了监管要素的复杂相互作用,包括启动上下文和uORFs,影响翻译.
- 证明仅在合成数据上训练的模型无法充分解释5'UTR介导的翻译调节.
- 表明合成数据缺乏自然5'UTR中发现的动机频率,限制了预测准确度.
结论:
- 模型解释对于理解模型行为,数据属性和mRNA翻译至关重要.
- 结合合成和人类数据的OptMRL模型更好地捕捉了人类翻译规范的特点.
- 这种方法为构建有效的基于序列的基因调节模型提供了总体策略,有助于疾病理解和翻译工程.
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