BP neural network (BPNN), as a multilayer feed-forward network, can realize the deep cognition to target data and high accuracy to output results. However, there were still no related research of k-mer based on BPNN yet. In present study, BPNN was used to train and test binary classification data of each classification mode respectively. All k-mer were divided into two categories according to the X + Y content or completely random mode. Results showed that 1) For classification mode of X + Y content, the accuracy of k-mers classification was 100 percent, no matter k ≤ 6 or k ≥ 7; 2) For completely random classification mode, the accuracy of classification is 100 percent for k-mers of k ≤ 6; But for k-mers of k ≥ 7, the accuracy is less than 100 percent, and with the increase of k value, the accuracy of classification gradually decreases (gradually approaches 50 percent). The k-mers of k ≥ 7 should be the basic functional fragment of nucleic acid, and perform basic nucleic acid function in the DNA sequence. The k-mers of k ≤ 6 should be the basic component fragment of nucleic acid, and no longer perform basic nucleic acid function.

中文翻译：

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基于BPNN的K-Mer最小功能长度分析

BP神经网络（BPNN）作为一种多层前馈网络，可以实现对目标数据的深度认知和输出结果的高精度。但是，目前还没有基于 BPNN 的 k-mer 的相关研究。在本研究中，BPNN分别用于训练和测试每种分类模式的二元分类数据。所有k-mer根据X + Y含量或完全随机模式分为两类。结果表明：1）对于X+Y含量的分类方式，k-mers分类的准确率是100%，无论k ≤ 6 或k≥7；2）对于完全随机的分类模式，分类准确率为100%，对于k-mersk≤6；但对于 k-mersk ≥ 7，准确率小于 100%，并且随着k 值，分类准确率逐渐下降（逐渐接近 50%）。k聚体k≥7应该是核酸的基本功能片段，在DNA序列中发挥基本核酸功能。k聚体k≤6应该是核酸的基本组成片段，不再执行基本核酸功能。