Abstract
Classification of protein sequences is an important method to predict the structure and function of novel protein sequences. The determination of protein function has a very important role in promoting both disease prevention and drug development. With the continuous development of bioinformatics and the large accumulation of related data, the functional prediction of unknown proteins using scientific computational methods has become an important research topic in bioinformatics in the post-genomic era, so the classification algorithm of protein sequences has also become one of the primary tasks of the current life science research. In this paper, we try to use two classical classification algorithms, LetNet-5 and VGG16, to study the classification problem of protein sequences.
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Acknowledgement
This work was supported by the Natural Science Foundation of China (No. 61902337), the fundamental Research Funds for the Central Universities, 2020QN89, Xuzhou science and technology plan project, KC19142, KC21047, Jiangsu Provincial Natural Science Foundation (No. SBK2019040953), Natural Science Fund for Colleges and Universities in Jiangsu Province (No. 19KJB520016) and Young talents of science and technology in Jiangsu. Zheng Tao, Zhen Yang, and Baitong Chen can be treated as the co-first authors.
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Tao, Z., Yang, Z., Chen, B., Bao, W., Cheng, H. (2022). Protein Sequence Classification with LetNet-5 and VGG16. 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_60
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DOI: https://doi.org/10.1007/978-3-031-13829-4_60
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