The General Explanation Method with NMR Spectroscopy Enables the Identification of Metabolite Profiles Specific for Normal and Tumor Cell Lines.

ABSTRACT: Machine learning models in metabolomics, despite their great prediction accuracy, are still not widely adopted owing to the lack of an efficient explanation for their predictions. In this study, we propose the use of the general explanation method to explain the predictions of a machine learning model to gain detailed insight into metabolic differences between biological systems. The method was tested on a dataset of ¹ H NMR spectra acquired on normal lung and mesothelial cell lines and their tumor counterparts. Initially, the random forests and artificial neural network models were applied to the dataset, and excellent prediction accuracy was achieved. The predictions of the models were explained with the general explanation method, which enabled identification of discriminatin

SUBMITTER: Pecnik K 

PROVIDER: S-EPMC6220813 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature