Protein acetylation sites with complex-valued polynomial model

doi:10.1007/s11704-023-2640-9

Front. Comput. Sci.

2024, Vol. 18

Issue (3) : 183904 https://doi.org/10.1007/s11704-023-2640-9

Interdisciplinary

Protein acetylation sites with complex-valued polynomial model

Wenzheng BAO¹, Bin YANG²(

)

¹. School of Information Engineering, Xuzhou University of Technology, Xuzhou 221018, China
². School of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China

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Abstract

Protein acetylation refers to a process of adding acetyl groups (CH3CO-) to lysine residues on protein chains. As one of the most commonly used protein post-translational modifications, lysine acetylation plays an important role in different organisms. In our study, we developed a human-specific method which uses a cascade classifier of complex-valued polynomial model (CVPM), combined with sequence and structural feature descriptors to solve the problem of imbalance between positive and negative samples. Complex-valued gene expression programming and differential evolution are utilized to search the optimal CVPM model. We also made a systematic and comprehensive analysis of the acetylation data and the prediction results. The performances of our proposed method are 79.15% in S_p, 78.17% in S_n, 78.66% in ACC 78.76% in F1, and 0.5733 in MCC, which performs better than other state-of-the-art methods.

Keywords protein acetylation complex-valued polynomial model machine learning

Corresponding Author(s): Bin YANG

Just Accepted Date: 30 December 2022 Issue Date: 17 April 2023

Cite this article:

Wenzheng BAO,Bin YANG. Protein acetylation sites with complex-valued polynomial model[J]. Front. Comput. Sci., 2024, 18(3): 183904.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-023-2640-9
https://academic.hep.com.cn/fcs/EN/Y2024/V18/I3/183904

Fig.1 The outlines of our proposed method

Fig.2 An example of the chromosome in CVGEP (a), and the expression tree decoded (b)

Fig.3 The flowchart of CVGEP

Fig.4 The Sensitivity of each model

Fig.5 The Specificity of each model

Fig.6 The Accuracy of each model

Fig.7 The MCC of each model

Fig.8 The Sensitivity of each feature

Fig.9 The Specificity of each feature

Fig.10 The Accuracy of each feature

Fig.11 The MCC of each feature

Tab.1 The performances of different classification models

Fig.12 ROC curves of six methods

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