关于最小化器和卷积过器:对基因组分析的理论联系和应用
1Department of Mathematics, University of Toronto, Toronto, Ontario, Canada.
概括
随机高斯初始化和最大聚合的卷积神经网络 (CNN) 在数学上相当于基于最小化器的分析生物序列的方法. 这种等价性解释了它们的有效性,特别是在重复的基因组区域.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- 缩小器和卷积神经网络 (CNN) 是分析生物序列的不同技术.
- 最小化器使用k-mer散列,而CNN则使用过器和聚合器进行特征提取和分类.
研究的目的:
- 以数学分析连接最小化器和CNN的哈希函数属性.
- 解释CNN在分类序列分析中的有效性.
主要方法:
- 哈希函数属性的数学分析.
- 在模拟和真实生物序列 (人类端粒) 上进行实证实验.
- 训练一个CNN嵌入SARS-CoV-2基因组短读.
主要成果:
- 随机高斯初始化CNN过器与最大共享相当于特定的最小化订单.
- 这种等价性导致重复序列区域的k-mer密度下降.
- 一个CNN嵌入的SARS-CoV-2在本地读取回合序列距离,虽然目前不切实际.
结论:
- 为生物序列分析中的CNN有效性提供了部分数学解释.
- 突出了看似不相似的序列分析技术之间的联系.
- 暗示了在序列组装中深度学习的潜力.
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