NCAE:使用深度网络连贯DNA甲基化自编码器的数据驱动表示识别了强大的疾病和风险因素签名.
David Martínez-Enguita1, Sanjiv K Dwivedi1, Rebecka Jörnsten2
1Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Sweden.
Briefings in bioinformatics
|August 17, 2023
概括
本研究引入了一种新的数据驱动工作流,使用网络连贯自编码器 (NCAE) 进行DNA甲基化分析. 该方法识别了强大的疾病和风险因素特征,优于精准医学的现有方法.
科学领域:
- 表观遗传学 在表观遗传学中,表观遗传学是指表观遗传学.
- 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:
- 精准医学需要从OMIC数据中识别可靠的疾病和风险因素标志.
- 基于知识的方法可能会因为固有的偏见而错过新的生物学见解.
- 基因甲基化在基因调节和疾病发展中起着至关重要的作用.
研究的目的:
- 开发数据驱动的工作流程,使用网络连贯自编码器 (NCAE) 发现DNA甲基化特征.
- 为了确定风险因素 (衰老,吸烟) 和疾病 (系统性红斑狼) 的强有力的签名.
- 克服基于知识的方法在数据分析的局限性.
主要方法:
- 在一个大型的人类表观基因组范围的关联研究汇编 (n=75,272) 上探索了自编码器架构.
- 使用具有生物相关潜伏嵌入的网络连贯自编码器 (NCAE).
- 训练有素可解释的深度神经网络使用NCAE嵌入式用于预测任务.
主要成果:
- 在对应于生物网络模块的自编码器隐性空间中观察到共定位模式的出现.
- 在人类蛋白质互动组中发现了一种具有强烈协同定位和中心性信号的NCAE配置.
- 基于NCAE嵌入式模型的表现优于现有的预测器,揭示了新的DNA甲基化特征.
结论:
- 数据驱动的工作流提供了一个可概括的管道,用于从DNA甲基化数据中获取风险因素和疾病信息.
- 这种方法通过超越知识驱动方法的局限性来增强对复杂表观遗传过程的理解.
- 促进了对各种疾病的改进诊断和治疗策略的开发.
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