Cargando…
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...
Autores principales: | , , , |
---|---|
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 |
_version_ | 1784870902024896512 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9845409 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT chensisi adiseaserelatedessentialproteinpredictionmodelbasedonthetransferneuralnetwork AT huangchiguo adiseaserelatedessentialproteinpredictionmodelbasedonthetransferneuralnetwork AT wanglei adiseaserelatedessentialproteinpredictionmodelbasedonthetransferneuralnetwork AT zhoushunxian adiseaserelatedessentialproteinpredictionmodelbasedonthetransferneuralnetwork AT chensisi diseaserelatedessentialproteinpredictionmodelbasedonthetransferneuralnetwork AT huangchiguo diseaserelatedessentialproteinpredictionmodelbasedonthetransferneuralnetwork AT wanglei diseaserelatedessentialproteinpredictionmodelbasedonthetransferneuralnetwork AT zhoushunxian diseaserelatedessentialproteinpredictionmodelbasedonthetransferneuralnetwork |