A Deep Learning Approach to Analyze NMR Spectra of SH-SY5Y Cells for Alzheimer’s Disease Diagnosis

Filippo Costanti (), Arian Kola, Franco Scarselli, Daniela Valensin and Monica Bianchini
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Filippo Costanti: Department of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy
Arian Kola: Department of Biotechnology, Chemistry, and Pharmacy, University of Siena, 53100 Siena, Italy
Franco Scarselli: Department of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy
Daniela Valensin: Department of Biotechnology, Chemistry, and Pharmacy, University of Siena, 53100 Siena, Italy
Monica Bianchini: Department of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy

Mathematics, 2023, vol. 11, issue 12, 1-13

Abstract: The SH-SY5Y neuroblastoma cell line is often used as an in vitro model of neuronal function and is widely applied to study the molecular events leading to Alzheimer’s disease (AD). Indeed, recently, basic research on SH-SY5Y cells has provided interesting insights for the discovery of new drugs and biomarkers for improved AD treatment and diagnosis. At the same time, untargeted NMR metabolomics is widely applied to metabolic profile analysis and screening for differential metabolites, to discover new biomarkers. In this paper, a compression technique based on convolutional autoencoders is proposed, which can perform a high dimensionality reduction in the spectral signal (up to more than 300 times), maintaining informative features (guaranteed by a reconstruction error always smaller than 5%). Moreover, before compression, an ad hoc preprocessing method was devised to remedy the scarcity of available data. The compressed spectral data were then used to train some SVM classifiers to distinguish diseased from healthy cells, achieving an accuracy close to 78%, a significantly better performance with respect to using standard PCA-compressed data.

Keywords: Alzheimer’s disease; SH-SY5Y cells; nuclear magnetic resonance (NMR); convolutional autoencoders; embedding of NMR spectra (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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