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Lisitsyna, A. ; Moritz, F. ; Liu, Y.* ; Al Sadat, L.* ; Hauner, H.* ; Claussnitzer, M.* ; Schmitt-Kopplin, P. ; Forcisi, S.

Feature selection pipelines with classification for non-targeted metabolomics combining the neural network and genetic algorithm.

Anal. Chem. 94, 5474-5482 (2022)

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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.

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Non-targeted metabolomics via high-resolution mass spectrometry methods, such as direct infusion Fourier transform-ion cyclotron resonance mass spectrometry (DI-FT-ICR MS), produces data sets with thousands of features. By contrast, the number of samples is in general substantially lower. This disparity presents challenges when analyzing non-targeted metabolomics data sets and often requires custom methods to uncover information not always accessible via classical statistical techniques. In this work, we present a pipeline that combines a convolutional neural network with traditional statistical approaches and an adaptation of a genetic algorithm. The developed method was applied to a lifestyle intervention cohort data set, where subjects at risk of type 2 diabetes underwent an oral glucose tolerance test. Feature selection is the final result of the pipeline, achieved through classification of the data set via a neural network, with a precision-recall score of over 0.9 on the test set. The features most relevant for the described classification were then chosen via a genetic algorithm. The output of the developed pipeline encompasses approximately 200 features with high predictive scores, providing a fingerprint of the metabolic changes in the prediabetic class on the data set. Our framework presents a new approach which allows to apply complex modeling based on convolutional neural networks for the analysis of high-resolution mass spectrometric data.

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