利用DNA的物理化学特征进行转录因子结合位点分类的深度卷积:利用深度卷积进行DNA-蛋白质分类的物理化学特征

Gergely Pap, Krisztian Adam, Zoltan Gyorgypal, Laszlo Toth, Z. Hegedus
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引用次数: 0

摘要

随着深度学习的进步,基于核苷酸表示对DNA序列进行分类已经取得了相当大的成功,因为正确使用和组合不同的层和架构选择导致了性能的提高。最常见的方法依赖于卷积层、循环层和注意层类型。此外,除了核苷酸序列之外,包含进一步的信息可以提高性能,尽管将输入特征表示与不同模型结构相结合的方法可能会带来挑战。为了研究这一主题,我们将深度可分卷积层应用于物理化学DNA序列表示和训练模型,以检测DNA结合蛋白的结合位点。当卷积核学习图案的局部特征模式时,深度可分离卷积的行为更好地利用了可以存储在输入表示中的特征、形状和物理化学信息。与卷积和基于核苷酸的方法相比,我们的深度可分离卷积模型在几个数据集上实现了准确性的提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Depthwise Convolutions using Physicochemical Features of DNA for Transcription Factor Binding Site Classification: Physicochemical Features for DNA-Protein Classification with Depthwise Convolutions
Classifying DNA sequences based on a nucleotide representation has enjoyed considerable success with the advancement of Deep Learning, as the proper usage and combination of different layers and architecture choices led to an increase in performance. The most common approaches rely on convolutional, recurrent and attention layer types. Moreover, the inclusion of further information in addition to the nucleotide sequence provides increases in performance, even though the methods of combining the input feature representations with distinct model structures could pose a challenge. To examine this topic, we applied depthwise separable convolutional layers to a physicochemical DNA sequence representation and train models to detect the binding sites of DNA binding proteins. While convolutional kernels learn the local feature patterns of motifs, the behaviour of the depthwise separable convolution better exploits the feature, shape and physicochemical information that could be stored in the input representation. Our models with depthwise separable convolution achieve increases in accuracy compared to the convolutional and nucleotide-based approaches on several datasets.
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