A deep multiple kernel learning-based higher-order fuzzy inference system for identifying DNA N4-methylcytosine sites

N4-methylcytosine (4mC), as a DNA modification, plays a crucial role in epigenetic regulation. However, the existing experimentation methods for accurately identifying 4mC sites are inefficient and highly consumable, making them difficult to implement. Although a variety of new identification methods are continuously being proposed, existing techniques are not yet fully mature. Compared to traditional 4mC site predictors, based on support vector machine or convolutional neural network, we present an alternative computational approach. In this study, we propose a method based on a kernelized higher-order fuzzy inference system (KHFIS) and deep multiple kernel learning, called DMKL-HFIS, to improve the accuracy of 4mC site identification DNA sequences. We use PSTNP to process the benchmark datasets, and then apply KHFIS to obtain multiple fuzzy kernel matrices. A deep neural network is used to fuse multiple fuzzy kernel matrices. Finally, the predicted value is derived from the fused matrix. Our approach was compared with existing mainstream computational methods. On the benchmark datasets (G. subterraneus, D. melanogaster, E. coli, A. thaliana, and C. elegans), the accuracy of our approach exceeded that of a state-of-the-art method by 0.4%, 0.44%, 1.51%, 0.55%, and 0.25%, respectively. Compared to mainstream methods, our approach exhibits a higher level of accuracy and can therefore be considered an effective prediction tool.