Prediction of Alzheimer’s Disease by a Novel Image-Based Representation of Gene Expression
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Otros documentos de la autoría: KALKAN, Habil; Akkaya, Umit Murat; Inal-Gültekin, Güldal; Sánchez-Pérez, Ana María
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/36080
comunitat-uji-handle3:10234/36082
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Título
Prediction of Alzheimer’s Disease by a Novel Image-Based Representation of Gene ExpressionFecha de publicación
2022Editor
MDPICita bibliográfica
Kalkan, H.; Akkaya, U.M.; Inal-Gültekin, G.; Sanchez-Perez, A.M. Prediction of Alzheimer’s Disease by a Novel Image-Based Representation of Gene Expression. Genes 2022, 13, 1406. https:// doi.org/10.3390/genes13081406Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Early intervention can delay the progress of Alzheimer’s Disease (AD), but currently, there are no effective prediction tools. The goal of this study is to generate a reliable artificial intelligence (AI) model capable ... [+]
Early intervention can delay the progress of Alzheimer’s Disease (AD), but currently, there are no effective prediction tools. The goal of this study is to generate a reliable artificial intelligence (AI) model capable of detecting the high risk of AD, based on gene expression arrays from blood samples. To that end, a novel image-formation method is proposed to transform single-dimension gene expressions into a discriminative 2-dimensional (2D) image to use convolutional neural networks (CNNs) for classification. Three publicly available datasets were pooled, and a total of 11,618 common genes’ expression values were obtained. The genes were then categorized for their discriminating power using the Fisher distance (AD vs. control (CTL)) and mapped to a 2D image by linear discriminant analysis (LDA). Then, a six-layer CNN model with 292,493 parameters were used for classification. An accuracy of 0.842 and an area under curve (AUC) of 0.875 were achieved for the AD vs. CTL classification. The proposed method obtained higher accuracy and AUC compared with other reported methods. The conversion to 2D in CNN offers a unique advantage for improving accuracy and can be easily transferred to the clinic to drastically improve AD (or any disease) early detection. [-]
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Genes 2022, 13(8), 1406Derechos de acceso
info:eu-repo/semantics/openAccess
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