Prediction of conotoxin type based on long short-term memory network

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 p...

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Autores principales:	Feng Wang, Shan Chang, Dashun Wei
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	conotoxin lstm character embedding spirotoxin category prediction Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/049c1f6da8a548d9bd0dfb9d24d17479
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id	oai:doaj.org-article:049c1f6da8a548d9bd0dfb9d24d17479
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spelling	oai:doaj.org-article:049c1f6da8a548d9bd0dfb9d24d174792021-11-12T01:39:17ZPrediction of conotoxin type based on long short-term memory network10.3934/mbe.20213321551-0018https://doaj.org/article/049c1f6da8a548d9bd0dfb9d24d174792021-08-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021332?viewType=HTMLhttps://doaj.org/toc/1551-0018Aiming 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.Feng WangShan Chang Dashun Wei AIMS Pressarticleconotoxinlstmcharacter embeddingspirotoxin categorypredictionBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 5, Pp 6700-6708 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	conotoxin lstm character embedding spirotoxin category prediction Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	conotoxin lstm character embedding spirotoxin category prediction Biotechnology TP248.13-248.65 Mathematics QA1-939 Feng Wang Shan Chang Dashun Wei Prediction of conotoxin type based on long short-term memory network
description	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.
format	article
author	Feng Wang Shan Chang Dashun Wei
author_facet	Feng Wang Shan Chang Dashun Wei
author_sort	Feng Wang
title	Prediction of conotoxin type based on long short-term memory network
title_short	Prediction of conotoxin type based on long short-term memory network
title_full	Prediction of conotoxin type based on long short-term memory network
title_fullStr	Prediction of conotoxin type based on long short-term memory network
title_full_unstemmed	Prediction of conotoxin type based on long short-term memory network
title_sort	prediction of conotoxin type based on long short-term memory network
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/049c1f6da8a548d9bd0dfb9d24d17479
work_keys_str_mv	AT fengwang predictionofconotoxintypebasedonlongshorttermmemorynetwork AT shanchang predictionofconotoxintypebasedonlongshorttermmemorynetwork AT dashunwei predictionofconotoxintypebasedonlongshorttermmemorynetwork
_version_	1718431269009752064

Prediction of conotoxin type based on long short-term memory network

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