DNA-N6 methyladenine (6-mA) methylation modification is one of the most important epigenetic modification markers. The aberrant 6-mA modification can affect gene expression and lead to serious diseases. Therefore, the work of predicting the 6-mA site is of great significance for the understanding of the pathogenesis and treatment of diseases. In this paper, a long short-term memory (LSTM) neural network based on K-mer encoding method and one hot encoding method is proposed to predict methylation sites.Firstly, the information content of gene sequence is increased through K-mer coding method. Secondly, the information content after one hot encoding is converted into a composite encoding matrix. The LSTM model can extract more feature dimensions and types from the encoding matrix, to improve the prediction performance of the LSTM model for gene sequence. The cross validation experiment show that the proposed method can achieve accuracy of 93.7% on benchmark datasets. The sensitivity, specificity and matthews correlation coefficient of the trained model were 93.0%, 94.5% and 0.875, which outperformed existing 6-mA prediction methods. On the other six different species datasets, the proposed method can achieve the area under the curve (AUC) values from 0.9055 to 0.9262,which shows the applicability of the proposed method on animals, plants and microorganisms methylation tasks. The proposed method was applied on rice gene NC_ 029258.1, and the predictions were verified by the recently published online prediction tools. The results show that 86% to 96% of the prediction results are supported by these tools, indicating that the proposed method can be applied to large-scale site prediction and analysis of different species.

The Vehicle Allocation Problem (VAP) in the vehicle sales chain has three bottlenecks in practice. The first is to collect relevant cooperation or conflict information, the second is to accurately quantify and analyze other factors affecting the distribution of cars, and the third is to establish a stable and rapid response to the vehicle allocation management method. In order to improve the real-time performance and reliability of vehicle allocation in the vehicle sales chain, it is crucial to find a method that can respond quickly and stabilize the vehicle allocation strategy. Therefore, this paper addresses these issues by extending Group Multi-Role Assignment with Cooperation and Conflict Factors (GMRACCF) from a new perspective. Through the logical reasoning of closure computation, the KD45 logic algorithm is used to find the implicit cognitive Cooperation and Conflict Factors (CCF). Therefore, a collaborative filtering comprehensive evaluation method is proposed to help administrators determine the influence weight of CCFs and Cooperation Scales (CSs) on the all-round performance according to their needs. Based on collaborative filtering, semantic modification is applied to resolve conflicts among qualifications. Large-scale simulation results show that the proposed method is feasible and robust, and provides a reliable decision-making reference in the vehicle sales chain.