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

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...
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Gespeichert in:

Autor*in:	Filippo Costanti [verfasserIn] Arian Kola [verfasserIn] Franco Scarselli [verfasserIn] Daniela Valensin [verfasserIn] Monica Bianchini [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Alzheimer’s disease SH-SY5Y cells nuclear magnetic resonance (NMR) convolutional autoencoders embedding of NMR spectra

Übergeordnetes Werk:	In: Mathematics - MDPI AG, 2013, 11(2023), 12, p 2664
Übergeordnetes Werk:	volume:11 ; year:2023 ; number:12, p 2664

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/math11122664

Katalog-ID:	DOAJ094106762

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language	English
source	In Mathematics 11(2023), 12, p 2664 volume:11 year:2023 number:12, p 2664
sourceStr	In Mathematics 11(2023), 12, p 2664 volume:11 year:2023 number:12, p 2664
format_phy_str_mv	Article
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topic_facet	Alzheimer’s disease SH-SY5Y cells nuclear magnetic resonance (NMR) convolutional autoencoders embedding of NMR spectra Mathematics
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container_title	Mathematics
authorswithroles_txt_mv	Filippo Costanti @@aut@@ Arian Kola @@aut@@ Franco Scarselli @@aut@@ Daniela Valensin @@aut@@ Monica Bianchini @@aut@@
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callnumber-first	Q - Science
author	Filippo Costanti
spellingShingle	Filippo Costanti misc QA1-939 misc Alzheimer’s disease misc SH-SY5Y cells misc nuclear magnetic resonance (NMR) misc convolutional autoencoders misc embedding of NMR spectra misc Mathematics A Deep Learning Approach to Analyze NMR Spectra of SH-SY5Y Cells for Alzheimer’s Disease Diagnosis
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topic_title	QA1-939 A Deep Learning Approach to Analyze NMR Spectra of SH-SY5Y Cells for Alzheimer’s Disease Diagnosis Alzheimer’s disease SH-SY5Y cells nuclear magnetic resonance (NMR) convolutional autoencoders embedding of NMR spectra
topic	misc QA1-939 misc Alzheimer’s disease misc SH-SY5Y cells misc nuclear magnetic resonance (NMR) misc convolutional autoencoders misc embedding of NMR spectra misc Mathematics
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title	A Deep Learning Approach to Analyze NMR Spectra of SH-SY5Y Cells for Alzheimer’s Disease Diagnosis
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title_auth	A Deep Learning Approach to Analyze NMR Spectra of SH-SY5Y Cells for Alzheimer’s Disease Diagnosis
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 <i<ad hoc</i< 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.
abstractGer	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 <i<ad hoc</i< 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.
abstract_unstemmed	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 <i<ad hoc</i< 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.
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title_short	A Deep Learning Approach to Analyze NMR Spectra of SH-SY5Y Cells for Alzheimer’s Disease Diagnosis
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