Cargando…

Combining genetic risk score with artificial neural network to predict the efficacy of folic acid therapy to hyperhomocysteinemia

Artificial neural network (ANN) is the main tool to dig data and was inspired by the human brain and nervous system. Several studies clarified its application in medicine. However, none has applied ANN to predict the efficacy of folic acid treatment to Hyperhomocysteinemia (HHcy). The efficacy has b...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Xiaorui, Huang, Xiaowen, Jie, Diao, Zheng, Caifang, Wang, Xiliang, Zhang, Bowen, Shao, Weihao, Wang, Gaili, Zhang, Weidong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8563886/
https://www.ncbi.nlm.nih.gov/pubmed/34728708
http://dx.doi.org/10.1038/s41598-021-00938-8
_version_ 1784593500201811968
author Chen, Xiaorui
Huang, Xiaowen
Jie, Diao
Zheng, Caifang
Wang, Xiliang
Zhang, Bowen
Shao, Weihao
Wang, Gaili
Zhang, Weidong
author_facet Chen, Xiaorui
Huang, Xiaowen
Jie, Diao
Zheng, Caifang
Wang, Xiliang
Zhang, Bowen
Shao, Weihao
Wang, Gaili
Zhang, Weidong
author_sort Chen, Xiaorui
collection PubMed
description Artificial neural network (ANN) is the main tool to dig data and was inspired by the human brain and nervous system. Several studies clarified its application in medicine. However, none has applied ANN to predict the efficacy of folic acid treatment to Hyperhomocysteinemia (HHcy). The efficacy has been proved to associate with both genetic and environmental factors while previous studies just focused on the latter one. The explained variance genetic risk score (EV-GRS) had better power and could represent the effect of genetic architectures. Our aim was to add EV-GRS into environmental factors to establish ANN to predict the efficacy of folic acid therapy to HHcy. We performed the prospective cohort research enrolling 638 HHcy patients. The multilayer perception algorithm was applied to construct ANN. To evaluate the effect of ANN, we also established logistic regression (LR) model to compare with ANN. According to our results, EV-GRS was statistically associated with the efficacy no matter analyzed as a continuous variable (OR = 3.301, 95%CI 1.954–5.576, P < 0.001) or category variable (OR = 3.870, 95%CI 2.092–7.159, P < 0.001). In our ANN model, the accuracy was 84.78%, the Youden’s index was 0.7073 and the AUC was 0.938. These indexes above indicated higher power. When compared with LR, the AUC, accuracy, and Youden’s index of the ANN model (84.78%, 0.938, 0.7073) were all slightly higher than the LR model (83.33% 0.910, 0.6687). Therefore, clinical application of the ANN model may be able to better predict the folic acid efficacy to HHcy than the traditional LR model. When testing two models in the validation set, we got the same conclusion. This study appears to be the first one to establish the ANN model which added EV-GRS into environmental factors to predict the efficacy of folic acid to HHcy. This model would be able to offer clinicians a new method to make decisions and individual therapeutic plans.
format Online
Article
Text
id pubmed-8563886
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-85638862021-11-04 Combining genetic risk score with artificial neural network to predict the efficacy of folic acid therapy to hyperhomocysteinemia Chen, Xiaorui Huang, Xiaowen Jie, Diao Zheng, Caifang Wang, Xiliang Zhang, Bowen Shao, Weihao Wang, Gaili Zhang, Weidong Sci Rep Article Artificial neural network (ANN) is the main tool to dig data and was inspired by the human brain and nervous system. Several studies clarified its application in medicine. However, none has applied ANN to predict the efficacy of folic acid treatment to Hyperhomocysteinemia (HHcy). The efficacy has been proved to associate with both genetic and environmental factors while previous studies just focused on the latter one. The explained variance genetic risk score (EV-GRS) had better power and could represent the effect of genetic architectures. Our aim was to add EV-GRS into environmental factors to establish ANN to predict the efficacy of folic acid therapy to HHcy. We performed the prospective cohort research enrolling 638 HHcy patients. The multilayer perception algorithm was applied to construct ANN. To evaluate the effect of ANN, we also established logistic regression (LR) model to compare with ANN. According to our results, EV-GRS was statistically associated with the efficacy no matter analyzed as a continuous variable (OR = 3.301, 95%CI 1.954–5.576, P < 0.001) or category variable (OR = 3.870, 95%CI 2.092–7.159, P < 0.001). In our ANN model, the accuracy was 84.78%, the Youden’s index was 0.7073 and the AUC was 0.938. These indexes above indicated higher power. When compared with LR, the AUC, accuracy, and Youden’s index of the ANN model (84.78%, 0.938, 0.7073) were all slightly higher than the LR model (83.33% 0.910, 0.6687). Therefore, clinical application of the ANN model may be able to better predict the folic acid efficacy to HHcy than the traditional LR model. When testing two models in the validation set, we got the same conclusion. This study appears to be the first one to establish the ANN model which added EV-GRS into environmental factors to predict the efficacy of folic acid to HHcy. This model would be able to offer clinicians a new method to make decisions and individual therapeutic plans. Nature Publishing Group UK 2021-11-02 /pmc/articles/PMC8563886/ /pubmed/34728708 http://dx.doi.org/10.1038/s41598-021-00938-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Chen, Xiaorui
Huang, Xiaowen
Jie, Diao
Zheng, Caifang
Wang, Xiliang
Zhang, Bowen
Shao, Weihao
Wang, Gaili
Zhang, Weidong
Combining genetic risk score with artificial neural network to predict the efficacy of folic acid therapy to hyperhomocysteinemia
title Combining genetic risk score with artificial neural network to predict the efficacy of folic acid therapy to hyperhomocysteinemia
title_full Combining genetic risk score with artificial neural network to predict the efficacy of folic acid therapy to hyperhomocysteinemia
title_fullStr Combining genetic risk score with artificial neural network to predict the efficacy of folic acid therapy to hyperhomocysteinemia
title_full_unstemmed Combining genetic risk score with artificial neural network to predict the efficacy of folic acid therapy to hyperhomocysteinemia
title_short Combining genetic risk score with artificial neural network to predict the efficacy of folic acid therapy to hyperhomocysteinemia
title_sort combining genetic risk score with artificial neural network to predict the efficacy of folic acid therapy to hyperhomocysteinemia
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8563886/
https://www.ncbi.nlm.nih.gov/pubmed/34728708
http://dx.doi.org/10.1038/s41598-021-00938-8
work_keys_str_mv AT chenxiaorui combininggeneticriskscorewithartificialneuralnetworktopredicttheefficacyoffolicacidtherapytohyperhomocysteinemia
AT huangxiaowen combininggeneticriskscorewithartificialneuralnetworktopredicttheefficacyoffolicacidtherapytohyperhomocysteinemia
AT jiediao combininggeneticriskscorewithartificialneuralnetworktopredicttheefficacyoffolicacidtherapytohyperhomocysteinemia
AT zhengcaifang combininggeneticriskscorewithartificialneuralnetworktopredicttheefficacyoffolicacidtherapytohyperhomocysteinemia
AT wangxiliang combininggeneticriskscorewithartificialneuralnetworktopredicttheefficacyoffolicacidtherapytohyperhomocysteinemia
AT zhangbowen combininggeneticriskscorewithartificialneuralnetworktopredicttheefficacyoffolicacidtherapytohyperhomocysteinemia
AT shaoweihao combininggeneticriskscorewithartificialneuralnetworktopredicttheefficacyoffolicacidtherapytohyperhomocysteinemia
AT wanggaili combininggeneticriskscorewithartificialneuralnetworktopredicttheefficacyoffolicacidtherapytohyperhomocysteinemia
AT zhangweidong combininggeneticriskscorewithartificialneuralnetworktopredicttheefficacyoffolicacidtherapytohyperhomocysteinemia