{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T03:57:18Z","timestamp":1770782238056,"version":"3.50.0"},"reference-count":42,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,8,8]],"date-time":"2022-08-08T00:00:00Z","timestamp":1659916800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Genes"],"abstract":"<jats:p>Early intervention can delay the progress of Alzheimer\u2019s 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\u2019 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.<\/jats:p>","DOI":"10.3390\/genes13081406","type":"journal-article","created":{"date-parts":[[2022,8,8]],"date-time":"2022-08-08T21:09:18Z","timestamp":1659992958000},"page":"1406","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Prediction of Alzheimer\u2019s Disease by a Novel Image-Based Representation of Gene Expression"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5456-8385","authenticated-orcid":false,"given":"Habil","family":"Kalkan","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Gebze Technical University, 41400 Kocaeli, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Umit Murat","family":"Akkaya","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Gebze Technical University, 41400 Kocaeli, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G\u00fcldal","family":"Inal-G\u00fcltekin","sequence":"additional","affiliation":[{"name":"Department of Physiology, Faculty of Medicine, Istanbul Okan University, 34959 Istanbul, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ana Maria","family":"Sanchez-Perez","sequence":"additional","affiliation":[{"name":"Faculty of Health Science and Institute of Advanced Materials (INAM), University Jaume I, 12071 Castellon, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,8]]},"reference":[{"key":"ref_1","first-page":"34","article-title":"Early-onset Alzheimer Disease and Its Variants","volume":"25","author":"Mendez","year":"2019","journal-title":"Continuum"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1038\/ng1095-219","article-title":"The structure of the presenilin 1 (S182) gene and identification of six novel mutations in early onset AD families","volume":"11","author":"Clark","year":"1995","journal-title":"Nat. Genet."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"de la Vega, M.P., N\u00e4slund, C., Brundin, R., Lannfelt, L., L\u00f6wenmark, M., Kilander, L., Ingelsson, M., and Giedraitis, V. (2022). Mutation analysis of disease-causing genes in patients with early onset or familial forms of Alzheimer\u2019s disease and frontotemporal dementia. BMC Genom., 23.","DOI":"10.1186\/s12864-022-08343-9"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1017\/S0317167100013949","article-title":"Early-Onset Familial Alzheimer\u2019s Disease (EOFAD)","volume":"39","author":"Wu","year":"2012","journal-title":"Can. J. Neurol. Sci. J. Can. Sci. Neurol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"535","DOI":"10.2147\/CIA.S51571","article-title":"The genetics of Alzheimer\u2019s disease","volume":"9","author":"Bagyinszky","year":"2014","journal-title":"Clin. Interv. Aging"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1146\/annurev-pathmechdis-030421-112756","article-title":"Apolipoprotein E and Alzheimer\u2019s disease: Findings, hypotheses, and potential mechanisms","volume":"17","author":"Koutsodendris","year":"2022","journal-title":"Annu. Rev. Pathol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1007\/s13311-021-01152-0","article-title":"Genomics and Functional Genomics of Alzheimer\u2019s Disease","volume":"19","author":"Kamboh","year":"2022","journal-title":"Neurotherapeutics"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Dib, S., Pahnke, J., and Gosselet, F. (2021). Role of ABCA7 in Human Health and in Alzheimer\u2019s Disease. Int. J. Mol. Sci., 22.","DOI":"10.3390\/ijms22094603"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/s13024-021-00505-9","article-title":"Challenge accepted: Uncovering the role of rare genetic variants in Alzheimer\u2019s disease","volume":"17","author":"Khani","year":"2022","journal-title":"Mol. Neurodegener."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"112106","DOI":"10.1016\/j.bbr.2019.112106","article-title":"Early intervention with ABA prevents neuroinflammation and memory impairment in a triple transgenic mice model of Alzheimer\u00b4s disease","volume":"374","year":"2019","journal-title":"Behav. Brain Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2255","DOI":"10.1016\/S0140-6736(15)60461-5","article-title":"A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): A randomised controlled trial","volume":"385","author":"Ngandu","year":"2015","journal-title":"Lancet"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1136\/bjsports-2021-104981","article-title":"Physical activity as a protective factor for dementia and Alzheimer\u2019s disease: Systematic review, meta-analysis and quality assessment of cohort and case-control studies","volume":"56","author":"Kujala","year":"2022","journal-title":"Br. J. Sports Med."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kumar, M.R., Azizi, N.F., Yeap, S.K., Abdullah, J.O., Khalid, M., Omar, A.R., Osman, M.A., Leow, A.T.C., Mortadza, S.A.S., and Alitheen, N.B. (2022). Clinical and Preclinical Studies of Fermented Foods and Their Effects on Alzheimer\u2019s Disease. Antioxidants, 11.","DOI":"10.3390\/antiox11050883"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.pharmthera.2019.03.005","article-title":"Gene-environment interactions in Alzheimer\u2019s disease: A potential path to precision medicine","volume":"199","author":"Eid","year":"2019","journal-title":"Pharmacol. Ther."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1038\/ng.2802","article-title":"Meta-Analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer\u2019s disease","volume":"45","author":"Lambert","year":"2013","journal-title":"Nat. Genet."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Escott-Price, V., Bellenguez, C., Wang, L.-S., Choi, S.-H., Harold, D., Jones, L., Holmans, P., Gerrish, A., Vedernikov, A., and Richards, A. (2014). Gene-Wide Analysis Detects Two New Susceptibility Genes for Alzheimer\u2019s Disease. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0094661"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"fcac125","DOI":"10.1093\/braincomms\/fcac125","article-title":"Genome-wide association studies for Alzheimer\u2019s disease: Bigger is not always better","volume":"4","author":"Hardy","year":"2022","journal-title":"Brain Commun."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"653","DOI":"10.3389\/fgene.2018.00653","article-title":"Prediction of Alzheimer\u2019s Disease-Associated Genes by Integration of GWAS Summary Data and Expression Data","volume":"9","author":"Hao","year":"2019","journal-title":"Front. Genet."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Farooq, A., Anwar, S., Awais, M., and Rehman, S. (2017, January 18\u201320). A deep CNN based multi-class classification of Alzheimer\u2019s disease using MRI. Proceedings of the IEEE International Conference on Imaging Systems and Techniques, Beijing, China.","DOI":"10.1109\/IST.2017.8261460"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compmedimag.2019.01.005","article-title":"RNN-based longitudinal analysis for diagnosis of Alzheimer\u2019s disease","volume":"73","author":"Cui","year":"2019","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3485","DOI":"10.1038\/s41598-020-60595-1","article-title":"Prediction of Alzheimer\u2019s disease using blood gene expression data","volume":"10","author":"Lee","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"784814","DOI":"10.3389\/fgene.2021.784814","article-title":"Improving the Classification of Alzheimer\u2019s Disease Using Hybrid Gene Selection Pipeline and Deep Learning","volume":"12","author":"Mahendran","year":"2021","journal-title":"Front. Genet."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"17394","DOI":"10.1038\/s41598-018-35789-3","article-title":"Systematic Analysis and Biomarker Study for Alzheimer\u2019s Disease","volume":"8","author":"Li","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s13244-018-0639-9","article-title":"Convolutional neural networks: An overview and application in radiology","volume":"9","author":"Yamashita","year":"2018","journal-title":"Insights Imaging"},{"key":"ref_25","unstructured":"Brigato, L., and Iocchi, L. (2020, January 10\u201315). A close look at deep learning with small data. Proceedings of the 25th International Conference on Pattern Recognition, Milan, Italy."},{"key":"ref_26","unstructured":"Sarraf, S., and Tofighi, G. (2016). Classification of Alzheimer\u2019s Disease Using Fmri Data and Deep Learning Convolutional Neural Networks. arXiv, Available online: https:\/\/arxiv.org\/abs\/1603.08631."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ji, H., Liu, Z., Yan, W.Q., and Klette, R. (2019, January 15\u201318). Early diagnosis of Alzheimer\u2019s disease using deep learning. Proceedings of the 2nd International Conference on Control and Computer Vision, Jeju, Korea.","DOI":"10.1145\/3341016.3341024"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/s10916-019-1475-2","article-title":"A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer\u2019s Disease Stages Using Resting-State fMRI and Residual Neural Networks","volume":"44","author":"Ramzan","year":"2020","journal-title":"J. Med. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1007\/s10278-019-00265-5","article-title":"Binary Classification of Alzheimer\u2019s Disease Using sMRI Imaging Modality and Deep Learning","volume":"33","author":"Ma","year":"2020","journal-title":"J. Digit. Imaging"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Marzban, E.N., Eldeib, A.M., Yassine, I.A., Kadah, Y.M., and Alzheimer\u2019s Disease Neurodegenerative Initiative (2020). Alzheimer\u2019s disease diagnosis from diffusion tensor images using convolutional neural networks. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0230409"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"11399","DOI":"10.1038\/s41598-019-47765-6","article-title":"DeepInsight: A methodology to transform a non-image data to an image for convolution neural network architecture","volume":"9","author":"Sharma","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_32","unstructured":"van der Maaten, L., and Hinton, G. (2008). Visualizing high-dimensional data using t-SNE. J. Mach. Learn. Res., 2579\u20132605."},{"key":"ref_33","unstructured":"Amari, S., and Nagaoka, H. (2000). Methods of Information Geometry, Translations of Mathematical Monographs, American Mathematical Society."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1186\/s13059-015-0750-x","article-title":"A novel multi-tissue RNA diagnostic of healthy ageing relates to cognitive health status","volume":"16","author":"Sood","year":"2015","journal-title":"Genome Biol."},{"key":"ref_35","unstructured":"(2022, July 09). Series GSE140829, Available online: https:\/\/www.ncbi.nlm.nih.gov\/geo\/query\/acc.cgi?acc=GSE140829."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Stat. Soc. Ser. B Methodol."},{"key":"ref_37","first-page":"1106","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"659","DOI":"10.3233\/JAD-150440","article-title":"A Pathway Based Classification Method for Analyzing Gene Expression for Alzheimer\u2019s Disease Diagnosis","volume":"49","author":"Voyle","year":"2016","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"El-Gawady, A., Makhlouf, M.A., Tawfik, B.S., and Nassar, H. (2022). Machine Learning Framework for the Prediction of Alzheimer\u2019s Disease Using Gene Expression Data Based on Efficient Gene Selection. Symmetry, 14.","DOI":"10.3390\/sym14030491"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"126","DOI":"10.19113\/sdufenbed.453462","article-title":"DNA microarray gene expression data classification using SVM, MLP, and RF with feature selection methods relief and LASSO","volume":"23","author":"Guckiran","year":"2019","journal-title":"SD\u00dc Bilim. Enst. Derg."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"157","DOI":"10.3389\/fgene.2019.00157","article-title":"Detecting Diagnostic Biomarkers of Alzheimer\u2019s Disease by Integrating Gene Expression Data in Six Brain Regions","volume":"10","author":"Wang","year":"2019","journal-title":"Front. Genet."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"112873","DOI":"10.1016\/j.eswa.2019.112873","article-title":"Prediction of Alzheimer\u2019s disease based on deep neural network by integrating gene expression and DNA methylation dataset","volume":"140","author":"Park","year":"2020","journal-title":"Expert Syst. Appl."}],"container-title":["Genes"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-4425\/13\/8\/1406\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:05:39Z","timestamp":1760141139000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-4425\/13\/8\/1406"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,8]]},"references-count":42,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["genes13081406"],"URL":"https:\/\/doi.org\/10.3390\/genes13081406","relation":{},"ISSN":["2073-4425"],"issn-type":[{"value":"2073-4425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,8]]}}}