Minimum functional length analysis of k-mer based on BP neural network.

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%, no matter k 6 or k 7; 2) For completely random classification mode, the accuracy of classification is 100% for k-mers of k 6; But for k-mers of k 7, the accuracy is less than 100%, and with the increase of k value, the accuracy of classification gradually decreases (gradually approaches 50%). 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.