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A disease-related essential protein prediction model based on the transfer neural network

Essential proteins play important roles in the development and survival of organisms whose mutations are proven to be the drivers of common internal diseases having higher prevalence rates. Due to high costs of traditional biological experiments, an improved Transfer Neural Network (TNN) was designe...

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Autores principales: Chen, Sisi, Huang, Chiguo, Wang, Lei, Zhou, Shunxian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9845409/
https://www.ncbi.nlm.nih.gov/pubmed/36685976
http://dx.doi.org/10.3389/fgene.2022.1087294
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author Chen, Sisi
Huang, Chiguo
Wang, Lei
Zhou, Shunxian
author_facet Chen, Sisi
Huang, Chiguo
Wang, Lei
Zhou, Shunxian
author_sort Chen, Sisi
collection PubMed
description Essential proteins play important roles in the development and survival of organisms whose mutations are proven to be the drivers of common internal diseases having higher prevalence rates. Due to high costs of traditional biological experiments, an improved Transfer Neural Network (TNN) was designed to extract raw features from multiple biological information of proteins first, and then, based on the newly-constructed Transfer Neural Network, a novel computational model called TNNM was designed to infer essential proteins in this paper. Different from traditional Markov chain, since Transfer Neural Network adopted the gradient descent algorithm to automatically obtain the transition probability matrix, the prediction accuracy of TNNM was greatly improved. Moreover, additional antecedent memory coefficient and bias term were introduced in Transfer Neural Network, which further enhanced both the robustness and the non-linear expression ability of TNNM as well. Finally, in order to evaluate the identification performance of TNNM, intensive experiments have been executed based on two well-known public databases separately, and experimental results show that TNNM can achieve better performance than representative state-of-the-art prediction models in terms of both predictive accuracies and decline rate of accuracies. Therefore, TNNM may play an important role in key protein prediction in the future.
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spelling pubmed-98454092023-01-19 A disease-related essential protein prediction model based on the transfer neural network Chen, Sisi Huang, Chiguo Wang, Lei Zhou, Shunxian Front Genet Genetics Essential proteins play important roles in the development and survival of organisms whose mutations are proven to be the drivers of common internal diseases having higher prevalence rates. Due to high costs of traditional biological experiments, an improved Transfer Neural Network (TNN) was designed to extract raw features from multiple biological information of proteins first, and then, based on the newly-constructed Transfer Neural Network, a novel computational model called TNNM was designed to infer essential proteins in this paper. Different from traditional Markov chain, since Transfer Neural Network adopted the gradient descent algorithm to automatically obtain the transition probability matrix, the prediction accuracy of TNNM was greatly improved. Moreover, additional antecedent memory coefficient and bias term were introduced in Transfer Neural Network, which further enhanced both the robustness and the non-linear expression ability of TNNM as well. Finally, in order to evaluate the identification performance of TNNM, intensive experiments have been executed based on two well-known public databases separately, and experimental results show that TNNM can achieve better performance than representative state-of-the-art prediction models in terms of both predictive accuracies and decline rate of accuracies. Therefore, TNNM may play an important role in key protein prediction in the future. Frontiers Media S.A. 2023-01-04 /pmc/articles/PMC9845409/ /pubmed/36685976 http://dx.doi.org/10.3389/fgene.2022.1087294 Text en Copyright © 2023 Chen, Huang, Wang and Zhou. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Chen, Sisi
Huang, Chiguo
Wang, Lei
Zhou, Shunxian
A disease-related essential protein prediction model based on the transfer neural network
title A disease-related essential protein prediction model based on the transfer neural network
title_full A disease-related essential protein prediction model based on the transfer neural network
title_fullStr A disease-related essential protein prediction model based on the transfer neural network
title_full_unstemmed A disease-related essential protein prediction model based on the transfer neural network
title_short A disease-related essential protein prediction model based on the transfer neural network
title_sort disease-related essential protein prediction model based on the transfer neural network
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9845409/
https://www.ncbi.nlm.nih.gov/pubmed/36685976
http://dx.doi.org/10.3389/fgene.2022.1087294
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