An Improved Stacked Autoencoder for Metabolomic Data Classification

• Published in: Computational intelligence and neuroscience Vol. 2021; no. 1; p. 1051172
• Main Authors: Fan, Xiaojing, Wang, Xiye, Jiang, Mingyang, Pei, Zhili, Qiao, Shicheng
• Format: Journal Article
• Language: English
• Published: New York Hindawi 2021; John Wiley & Sons, Inc
• Subjects: Algorithms; Analysis; Animal models; Arthritis; Biomarkers; Classification; Computational linguistics; Deep learning; Disease; Drug development; Feature selection; Language processing; Machine learning; Mean square errors; Medical prognosis; Medical research; Metabolomics; Natural language interfaces; Neural networks; Neurons; NMR; Nuclear magnetic resonance; Optimization; Principal components analysis; Rheumatoid arthritis; Rheumatoid factor; Side effects; Support vector machines; Training; Mongolia; China
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2021/1051172

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.