A novel model-based on FCM–LM algorithm for prediction of protein folding rate

Author:

Liu Longlong1,Ma Mingjiao1,Cui Jing1

Affiliation:

1. Department of Mathematics, Ocean University of China, Qingdao 266000, P. R. China

Abstract

The prediction of protein folding rates is of paramount importance in describing the protein folding mechanism, which has broad applications in fields such as enzyme engineering and protein engineering. Therefore, predicting protein folding rates using the first-order of protein sequence, secondary structure and amino acid properties has become a very active research topic in recent years. This paper presents a new fuzzy cognitive map (FCM) model based on deep learning neural networks which uses data obtained from biological experiments to predict the protein folding rate. FCM extracts the important data features from the protein sequence which then initializes the deep neural networks effectively. It was found that the Levenberg–Marquardt (LM) algorithm for deep neural networks can improve the prediction accuracy of the protein folding rates. The correlation coefficient between the predicted values and those real values obtained from experiments reached 0.94 and 0.9 in two independent numerical tests.

Funder

Ocean University of China

Publisher

World Scientific Pub Co Pte Lt

Subject

Computer Science Applications,Molecular Biology,Biochemistry

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Machine Learning-Enabled Estimation System Using Fuzzy Cognitive Mapping: A Review;Proceedings of Third International Conference on Computing, Communications, and Cyber-Security;2022-07-03

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3. An Effective Cumulative Torsion Angles Model for Prediction of Protein Folding Rates;Protein & Peptide Letters;2020-03-17

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