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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\u2013Marquardt (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.<\/jats:p>","DOI":"10.1142\/s0219720017500123","type":"journal-article","created":{"date-parts":[[2017,4,25]],"date-time":"2017-04-25T10:17:04Z","timestamp":1493115424000},"page":"1750012","source":"Crossref","is-referenced-by-count":4,"title":["A novel model-based on FCM\u2013LM algorithm for prediction of protein folding rate"],"prefix":"10.1142","volume":"15","author":[{"given":"Longlong","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Mathematics, Ocean University of China, Qingdao 266000, P. R. China"}]},{"given":"Mingjiao","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Ocean University of China, Qingdao 266000, P. R. 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