Abstract
Lysine malonylation is a newly discovered type of protein post-translational modification, which plays an essential role in many biological activities. A good knowledge of malonylation sites can serve as guidance in solving a large number of biological problems, such as disease diagnosis and drug discovery. There have already been several experimental approaches to identify modification sites, but they are relatively expensive. In this work, we propose three novel machine learning models and utilizes several effective feature description methods. The model is trained based on the cross validation method named Split to Equal Validation (SEV). The experiments show that our model outperforms the others considerably.
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Acknowledgments
This work was supported by the grants of the National Science Foundation of China, Nos. 61902337, 61702445, and the grant from the Ph.D. Programs Foundation of Ministry of Education of China (No. 20120072110040). The Shandong Provincial Natural Science Foundation, China (No. ZR2018LF005).
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Author Contribution Statement
W.B. conceived the method. Z.L designed the method. B.Y. designed the website of this algorithm. Y.Z. conducted the experiments and W.B. wrote the main manuscript text. All authors reviewed the manuscript.
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Sun, J., Bao, W., Cao, Y., Chen, Y. (2020). Classification of Protein Modification Sites with Machine Learning. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12464. Springer, Cham. https://doi.org/10.1007/978-3-030-60802-6_38
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DOI: https://doi.org/10.1007/978-3-030-60802-6_38
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