School of Information and Electrical Engineering, Xuzhou University of Technology, Xuzhou 221018, China.
School of Information Science and Engineering, Zaozhuang University, Zaozhuang 277100, China.
Int J Mol Sci. 2018 Dec 29;20(1):113. doi: 10.3390/ijms20010113.
Post-translational modification plays a key role in the field of biology. Experimental identification methods are time-consuming and expensive. Therefore, computational methods to deal with such issues overcome these shortcomings and limitations. In this article, we propose a lysine acetylation site identification with polynomial tree method (LAIPT), making use of the polynomial style to demonstrate amino-acid residue relationships in peptide segments. This polynomial style was enriched by the physical and chemical properties of amino-acid residues. Then, these reconstructed features were input into the employed classification model, named the flexible neural tree. Finally, some effect evaluation measurements were employed to test the model's performance.
翻译后修饰在生物学领域中起着关键作用。实验鉴定方法既耗时又昂贵。因此，用于处理此类问题的计算方法克服了这些缺点和局限性。在本文中，我们提出了一种利用多项式树方法识别赖氨酸乙酰化位点（LAIPT）的方法，该方法利用多项式样式来展示肽段中氨基酸残基的关系。这种多项式样式通过氨基酸残基的物理和化学性质得到了丰富。然后，将这些重构的特征输入到所采用的分类模型，即灵活神经树中。最后，采用了一些效果评估措施来测试模型的性能。
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