Abstract
DNA N6 methyladenine (6mA) is a widely studied and widespread epigenetic modification, which plays a vital role in cell growth and development. 6mA is present in many biological cellular processes, such as the regulation of gene expression and the rule of cross dialogue between transposon and histone modification. Therefore, in some biological research, the prediction of the 6mA site is very significant. Unfortunately, the existing biological experimental methods are expensive both in time and money. And they cannot meet the needs of existing research. So it is high time to develop a targeted and efficient computing model. Consequently, this paper proposes an intelligent and efficient calculation model i6mA-word2vec for the discrimination of 6mA sites. In our work, we use word2vec from the field of natural language processing to carry out distributed feature encoding. The word2vec model automatically represents the target class topic. Then, the extracted feature space was sent into the convolutional neural network as prediction input. The experimental prediction results show that our computational model has better performance.
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Acknowledgments
This work was supported in part by the University Innovation Team Project of Jinan (2019GXRC015), the Natural Science Foundation of Shandong Province, China (Grant No. ZR2021MF036).
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Fu, W., Zhong, Y., Chen, B., Cao, Y., Chen, J., Cong, H. (2022). i6mA-word2vec: A Newly Model Which Used Distributed Features for Predicting DNA N6-Methyladenine Sites in Genomes. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13394. Springer, Cham. https://doi.org/10.1007/978-3-031-13829-4_58
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