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An Improved Stacked Autoencoder for Metabolomic Data Classification
Naru3 (NR) is a traditional Mongolian medicine with high clinical efficacy and low incidence of side effects. Metabolomics is an approach that can facilitate the development of traditional drugs. However, metabolomic data have a high throughput, sparse, high-dimensional, and small sample nature, and...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8382558/ https://www.ncbi.nlm.nih.gov/pubmed/34434226 http://dx.doi.org/10.1155/2021/1051172 |
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author | Fan, Xiaojing Wang, Xiye Jiang, Mingyang Pei, Zhili Qiao, Shicheng |
author_facet | Fan, Xiaojing Wang, Xiye Jiang, Mingyang Pei, Zhili Qiao, Shicheng |
author_sort | Fan, Xiaojing |
collection | PubMed |
description | Naru3 (NR) is a traditional Mongolian medicine with high clinical efficacy and low incidence of side effects. Metabolomics is an approach that can facilitate the development of traditional drugs. However, metabolomic data have a high throughput, sparse, high-dimensional, and small sample nature, and their classification is challenging. Although deep learning methods have a wide range of applications, deep learning-based metabolomic studies have not been widely performed. We aimed to develop an improved stacked autoencoder (SAE) for metabolomic data classification. We established an NR-treated rheumatoid arthritis (RA) mouse model and classified the obtained metabolomic data using the Hessian-free SAE (HF-SAE) algorithm. During training, the unlabeled data were used for pretraining, and the labeled data were used for fine-tuning based on the HF algorithm for gradient descent optimization. The hybrid algorithm successfully classified the data. The results were compared with those of the support vector machine (SVM), k-nearest neighbor (KNN), and gradient descent SAE (GD-SAE) algorithms. A five-fold cross-validation was used to complete the classification experiment. In each fine-tuning process, the mean square error (MSE) and misclassification rates of the training and test data were recorded. We successfully established an NR animal model and an improved SAE for metabolomic data classification. |
format | Online Article Text |
id | pubmed-8382558 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-83825582021-08-24 An Improved Stacked Autoencoder for Metabolomic Data Classification Fan, Xiaojing Wang, Xiye Jiang, Mingyang Pei, Zhili Qiao, Shicheng Comput Intell Neurosci Research Article Naru3 (NR) is a traditional Mongolian medicine with high clinical efficacy and low incidence of side effects. Metabolomics is an approach that can facilitate the development of traditional drugs. However, metabolomic data have a high throughput, sparse, high-dimensional, and small sample nature, and their classification is challenging. Although deep learning methods have a wide range of applications, deep learning-based metabolomic studies have not been widely performed. We aimed to develop an improved stacked autoencoder (SAE) for metabolomic data classification. We established an NR-treated rheumatoid arthritis (RA) mouse model and classified the obtained metabolomic data using the Hessian-free SAE (HF-SAE) algorithm. During training, the unlabeled data were used for pretraining, and the labeled data were used for fine-tuning based on the HF algorithm for gradient descent optimization. The hybrid algorithm successfully classified the data. The results were compared with those of the support vector machine (SVM), k-nearest neighbor (KNN), and gradient descent SAE (GD-SAE) algorithms. A five-fold cross-validation was used to complete the classification experiment. In each fine-tuning process, the mean square error (MSE) and misclassification rates of the training and test data were recorded. We successfully established an NR animal model and an improved SAE for metabolomic data classification. Hindawi 2021-08-15 /pmc/articles/PMC8382558/ /pubmed/34434226 http://dx.doi.org/10.1155/2021/1051172 Text en Copyright © 2021 Xiaojing Fan et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Fan, Xiaojing Wang, Xiye Jiang, Mingyang Pei, Zhili Qiao, Shicheng An Improved Stacked Autoencoder for Metabolomic Data Classification |
title | An Improved Stacked Autoencoder for Metabolomic Data Classification |
title_full | An Improved Stacked Autoencoder for Metabolomic Data Classification |
title_fullStr | An Improved Stacked Autoencoder for Metabolomic Data Classification |
title_full_unstemmed | An Improved Stacked Autoencoder for Metabolomic Data Classification |
title_short | An Improved Stacked Autoencoder for Metabolomic Data Classification |
title_sort | improved stacked autoencoder for metabolomic data classification |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8382558/ https://www.ncbi.nlm.nih.gov/pubmed/34434226 http://dx.doi.org/10.1155/2021/1051172 |
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