Application of ensemble deep neural network to metabolomics studies

Abstract

Deep neural network (DNN) is a useful machine learning approach, although its applicability to metabolomics studies has rarely been explored. Here we describe the development of an ensemble DNN (EDNN) algorithm and its applicability to metabolomics studies. As a model case, the developed EDNN approach was applied to metabolomics data of various fish species collected from Japan coastal and estuarine environments for evaluation of a regression performance compared with conventional DNN, random forest, and support vector machine algorithms. This study also revealed that the metabolic profiles of fish muscles were correlated with fish size (growth) in a species-dependent manner. The performance of EDNN regression for fish size based on metabolic profiles was superior to that of DNN, random forest, and support vector machine algorithms. The EDNN approach, therefore, should be helpful for analyses of regression and concerns pertaining to classification in metabolomics studies.

Keywords: Deep neural network; Ensemble learning; Machine learning; Metabolomics; Nuclear magnetic resonance.