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DeepGP: An Integrated Deep Learning Method for Endocrine Disease Gene Prediction Using Omics Data
Endocrinology is the study focusing on hormones and their actions. Hormones are known as chemical messengers, released into the blood, that exert functions through receptors to make an influence in the target cell. The capacity of the mammalian organism to perform as a whole unit is made possible ba...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8290361/ https://www.ncbi.nlm.nih.gov/pubmed/34295899 http://dx.doi.org/10.3389/fcell.2021.700061 |
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author | Zhang, Ningyi Wang, Haoyan Xu, Chen Zhang, Liyuan Zang, Tianyi |
author_facet | Zhang, Ningyi Wang, Haoyan Xu, Chen Zhang, Liyuan Zang, Tianyi |
author_sort | Zhang, Ningyi |
collection | PubMed |
description | Endocrinology is the study focusing on hormones and their actions. Hormones are known as chemical messengers, released into the blood, that exert functions through receptors to make an influence in the target cell. The capacity of the mammalian organism to perform as a whole unit is made possible based on two principal control mechanisms, the nervous system and the endocrine system. The endocrine system is essential in regulating growth and development, tissue function, metabolism, and reproductive processes. Endocrine diseases such as diabetes mellitus, Grave’s disease, polycystic ovary syndrome, and insulin-like growth factor I deficiency (IGFI deficiency) are classical endocrine diseases. Endocrine dysfunction is also an increasing factor of morbidity in cancer and other dangerous diseases in humans. Thus, it is essential to understand the diseases from their genetic level in order to recognize more pathogenic genes and make a great effort in understanding the pathologies of endocrine diseases. In this study, we proposed a deep learning method named DeepGP based on graph convolutional network and convolutional neural network for prioritizing susceptible genes of five endocrine diseases. To test the performance of our method, we performed 10-cross-validations on an integrated reported dataset; DeepGP obtained a performance of the area under the curve of ∼83% and area under the precision-recall curve of ∼65%. We found that type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) share most of their associated genes; therefore, we should pay more attention to the rest of the genes related to T1DM and T2DM, respectively, which could help in understanding the pathogenesis and pathologies of these diseases. |
format | Online Article Text |
id | pubmed-8290361 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82903612021-07-21 DeepGP: An Integrated Deep Learning Method for Endocrine Disease Gene Prediction Using Omics Data Zhang, Ningyi Wang, Haoyan Xu, Chen Zhang, Liyuan Zang, Tianyi Front Cell Dev Biol Cell and Developmental Biology Endocrinology is the study focusing on hormones and their actions. Hormones are known as chemical messengers, released into the blood, that exert functions through receptors to make an influence in the target cell. The capacity of the mammalian organism to perform as a whole unit is made possible based on two principal control mechanisms, the nervous system and the endocrine system. The endocrine system is essential in regulating growth and development, tissue function, metabolism, and reproductive processes. Endocrine diseases such as diabetes mellitus, Grave’s disease, polycystic ovary syndrome, and insulin-like growth factor I deficiency (IGFI deficiency) are classical endocrine diseases. Endocrine dysfunction is also an increasing factor of morbidity in cancer and other dangerous diseases in humans. Thus, it is essential to understand the diseases from their genetic level in order to recognize more pathogenic genes and make a great effort in understanding the pathologies of endocrine diseases. In this study, we proposed a deep learning method named DeepGP based on graph convolutional network and convolutional neural network for prioritizing susceptible genes of five endocrine diseases. To test the performance of our method, we performed 10-cross-validations on an integrated reported dataset; DeepGP obtained a performance of the area under the curve of ∼83% and area under the precision-recall curve of ∼65%. We found that type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) share most of their associated genes; therefore, we should pay more attention to the rest of the genes related to T1DM and T2DM, respectively, which could help in understanding the pathogenesis and pathologies of these diseases. Frontiers Media S.A. 2021-07-06 /pmc/articles/PMC8290361/ /pubmed/34295899 http://dx.doi.org/10.3389/fcell.2021.700061 Text en Copyright © 2021 Zhang, Wang, Xu, Zhang and Zang. 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 | Cell and Developmental Biology Zhang, Ningyi Wang, Haoyan Xu, Chen Zhang, Liyuan Zang, Tianyi DeepGP: An Integrated Deep Learning Method for Endocrine Disease Gene Prediction Using Omics Data |
title | DeepGP: An Integrated Deep Learning Method for Endocrine Disease Gene Prediction Using Omics Data |
title_full | DeepGP: An Integrated Deep Learning Method for Endocrine Disease Gene Prediction Using Omics Data |
title_fullStr | DeepGP: An Integrated Deep Learning Method for Endocrine Disease Gene Prediction Using Omics Data |
title_full_unstemmed | DeepGP: An Integrated Deep Learning Method for Endocrine Disease Gene Prediction Using Omics Data |
title_short | DeepGP: An Integrated Deep Learning Method for Endocrine Disease Gene Prediction Using Omics Data |
title_sort | deepgp: an integrated deep learning method for endocrine disease gene prediction using omics data |
topic | Cell and Developmental Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8290361/ https://www.ncbi.nlm.nih.gov/pubmed/34295899 http://dx.doi.org/10.3389/fcell.2021.700061 |
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