The General Explanation Method with NMR Spectroscopy Enables the Identification of Metabolite Profiles Specific for Normal and Tumor Cell Lines. (14th September 2018)

• Record Type: Journal Article
• Authors: Pečnik, Klemen; Todorović, Vesna; Bošnjak, Maša; Čemažar, Maja; Kononenko, Igor; Serša, Gregor; Plavec, Janez
• Is Part Of: Chembiochem. Volume 19:Number 19(2018)
• Page Start: 2066
• Page End: 2071
• Publication Date: 2018-09-14
• Subjects: cancer -- general explanation method -- machine learning -- metabolomics -- NMR spectroscopy; Biochemistry -- Periodicals; Molecular biology -- Periodicals; Pharmaceutical chemistry -- Periodicals; 572
• DOI: 10.1002/cbic.201800392
• Languages: English
• ISSNs: 1439-4227

Abstract

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 discriminating metabolic concentration differences between individual cell lines and enabled the construction of their specific metabolic concentration profiles. This intuitive and robust method holds great promise for in‐depth understanding of the mechanisms that underline phenotypes as well as for biomarker discovery in complex diseases. Abstract : Explaining normal and tumor cell line differences : The random forests and artificial neural network models are trained on the NMR dataset of normal and tumor cell lines and achieve excellent prediction accuracy. The general explanation method is applied to explain the predictions of both models intuitively, and this enables identification of concentration differences of metabolites that discriminate normal and tumor cell line types.