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    魏梓轩, 周家乐. 基于VAE的编码DNA载体阻断事件聚类分析与研究[J]. 华东理工大学学报(自然科学版), 2020, 46(3): 411-418. DOI: 10.14135/j.cnki.1006-3080.20190424001
    引用本文: 魏梓轩, 周家乐. 基于VAE的编码DNA载体阻断事件聚类分析与研究[J]. 华东理工大学学报(自然科学版), 2020, 46(3): 411-418. DOI: 10.14135/j.cnki.1006-3080.20190424001
    WEI Zixuan, ZHOU Jiale. Clustering Analysis and Research on the Blockade Events of Encoded DNA Carrier Based on VAE[J]. Journal of East China University of Science and Technology, 2020, 46(3): 411-418. DOI: 10.14135/j.cnki.1006-3080.20190424001
    Citation: WEI Zixuan, ZHOU Jiale. Clustering Analysis and Research on the Blockade Events of Encoded DNA Carrier Based on VAE[J]. Journal of East China University of Science and Technology, 2020, 46(3): 411-418. DOI: 10.14135/j.cnki.1006-3080.20190424001

    基于VAE的编码DNA载体阻断事件聚类分析与研究

    Clustering Analysis and Research on the Blockade Events of Encoded DNA Carrier Based on VAE

    • 摘要: 纳米孔道检测技术是单分子检测领域一个重要的研究方向。对纳米孔道阻断电流信号进行特征提取和转换,是对阻断事件进行分类以确定分析物种类的关键。由于有监督学习只能对已知种类的阻断事件进行预测,难以实现对信号本质特征的区分,因此本文基于编码DNA载体的阻断事件,利用卷积神经网络的特征提取特性,提出了一种应用于纳米孔道信号的无监督聚类方法。结合深度嵌入聚类和变分自编码器(Variational Autoencoder, VAE),实现了对阻断事件的特征转换和聚类的整体性训练。实验结果表明,该聚类方法能对编码DNA载体阻断事件提供较好的聚类结果,与其他聚类算法相比,最高提升了29%的聚类精度,具有更高的聚类准确度。

       

      Abstract: Nanopore detection technology plays important role in the field of single molecule detection. The feature extraction and transformation of nanopore blockade current is the key to classify signals and determine the analytes. Since the supervised learning can only predict the blockade current with known types, the unsupervised learning would be expected to achieve the distinction of the essential characteristics of signals. By means of the blockade current based on DNA carrier and the feature extraction of convolutional neural network, this paper proposes an unsupervised clustering scheme for nanopore signals. The proposed method combines the improved deep embedded clustering with the variational auto-encoder so that the feature transformation and the clustering of the blockade current can be jointly trained. In the original improved deep embedded clustering, the model utilizes an auto-encoder to transform the input data to a new compressed representation, which can reserve the crucial information of the input data. Thus, the auto-encoder may be replaced by a variational auto-encoder to learn the parameters of a probability distribution on the input data, instead of just learning the compressed representation. Finally, the proposed clustering model is firstly verified via 8 clustering centroids. Due to the symmetrical encoded DNA, like “100” and “001”, the case of 6 clustering centroids is further conducted to validate the proposed clustering model. It is shown from the experiment results that the proposed method can provide better clustering results for blockade current of DNA carrier, and achieves a 29% boost at most, compared with other clustering algorithms. .

       

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