We-Kcr: a new protein crotonylation site prediction method based on sequence feature and neural network

Dan Li; Yanhe Wang; Yuhan Li

doi:10.1117/12.3021325

19 February 2024 We-Kcr: a new protein crotonylation site prediction method based on sequence feature and neural network

Dan Li, Yanhe Wang, Yuhan Li

Author Affiliations +

Proceedings Volume 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023); 130631W (2024) https://doi.org/10.1117/12.3021325
Event: Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 2023, Changchun, China

Abstract

Lysine crotonylation (Kcr) is a form of post-translational modification that can be widely found in both histones and non-histone proteins. Precise detection of Kcr is essential for a better understanding of their functional mechanisms and drug development studies. Computational algorithms can predict Kcr and provide reliable data support for experimental methods. In this study, an effective new neural network-based computational framework for Kcr prediction (We-Kcr) was designed on a large amount of protein sequence data. After validation assessment and method comparison, the results showed that the prediction model exhibits higher accuracy in predicting Kcr sites. The research in this work provides biologists to explore the functional and biological mechanisms of protein modification. The materials can be downloaded from the following website: https://github.com/lihh225/We-Kcr.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dan Li, Yanhe Wang, and Yuhan Li "We-Kcr: a new protein crotonylation site prediction method based on sequence feature and neural network", Proc. SPIE 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 130631W (19 February 2024); https://doi.org/10.1117/12.3021325

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