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Prediction of Intrinsically Disordered Proteins Based on Deep Neural Network-ResNet18

Jie Zhang, Jiaxiang Zhao*, Pengchang Xu
School of Electronic Information and Optical Engineering, Nankai University, Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Tianjin, 300350, China
* Corresponding Author: Jiaxiang Zhao. Email:
(This article belongs to this Special Issue: Computer Methods in Bio-mechanics and Biomedical Engineering)

Computer Modeling in Engineering & Sciences 2022, 131(2), 905-917. https://doi.org/10.32604/cmes.2022.019097

Received 02 September 2021; Accepted 01 November 2021; Issue published 14 March 2022

Abstract

Accurately, reliably and rapidly identifying intrinsically disordered (IDPs) proteins is essential as they often play important roles in various human diseases; moreover, they are related to numerous important biological activities. However, current computational methods have yet to develop a network that is sufficiently deep to make predictions about IDPs and demonstrate an improvement in performance. During this study, we constructed a deep neural network that consisted of five identical variant models, ResNet18, combined with an MLP network, for classification. Resnet18 was applied for the first time as a deep model for predicting IDPs, which allowed the extraction of information from IDP residues in greater detail and depth, and this information was then passed through the MLP network for the final identification process. Two well-known datasets, MXD494 and R80, were used as the blind independent datasets to compare their performance with that of our method. The simulation results showed that Matthew’s correlation coefficient obtained using our deep network model was 0.517 on the blind R80 dataset and 0.450 on the MXD494 dataset; thus, our method outperformed existing methods.

Keywords

ResNet18; MLP; intrinsically disordered protein

Cite This Article

Zhang, J., Zhao, J., Xu, P. (2022). Prediction of Intrinsically Disordered Proteins Based on Deep Neural Network-ResNet18. CMES-Computer Modeling in Engineering & Sciences, 131(2), 905–917.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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