Open AccessPrediction of conotoxin type based on long short-term memory network
Author(s) -
Feng Wang,
Shan Chang,
Dashun Wei
Publication year - 2021
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2021332
Subject(s) - sequence (biology) , conotoxin , computer science , embedding , set (abstract data type) , artificial intelligence , term (time) , representation (politics) , value (mathematics) , pattern recognition (psychology) , character (mathematics) , support vector machine , test set , algorithm , mathematics , machine learning , peptide , chemistry , biochemistry , physics , geometry , quantum mechanics , politics , political science , law , programming language
Aiming at the problems of the wet experiment method in identifying the types of conotoxins, such as the complexity, low efficiency and high cost, this study proposes a method that uses the sequence information of the conotoxin peptides combined with long short term memory networks (LSTM) models to predict the Methods of spirotoxin category. This method only needs to take the conotoxin peptide sequence as input, and adopts the character embedding method in text processing to automatically map the sequence to the feature vector representation, and the model extracts features for training and prediction. Experimental results show that the correct index of this method on the test set reaches 0.80, and the AUC value reaches 0.817. For the same test set, the AUC value of the KNN algorithm is 0.641, and the AUC value of the method proposed in this paper is 0.817, indicating that this method can effectively assist in identifying the type of conotoxin.