{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T10:11:37Z","timestamp":1792145497678,"version":"4.4.0"},"reference-count":59,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,10]],"date-time":"2022-04-10T00:00:00Z","timestamp":1649548800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Huntington Disease (HD) is a degenerative neurological disease that causes a significant impact on the quality of life of the patient and eventually death. In this paper we present an approach to create a biomarker using as an input DNA CpG methylation data to identify HD patients. DNA CpG methylation is a well-known epigenetic marker for disease state. Technological advances have made it possible to quickly analyze hundreds of thousands of CpGs. This large amount of information might introduce noise as potentially not all DNA CpG methylation levels will be related to the presence of the illness. In this paper, we were able to reduce the number of CpGs considered from hundreds of thousands to 237 using a non-linear approach. It will be shown that using only these 237 CpGs and non-linear techniques such as artificial neural networks makes it possible to accurately differentiate between control and HD patients. An underlying assumption in this paper is that there are no indications suggesting that the process is linear and therefore non-linear techniques, such as artificial neural networks, are a valid tool to analyze this complex disease. The proposed approach is able to accurately distinguish between control and HD patients using DNA CpG methylation data as an input and non-linear forecasting techniques. It should be noted that the dataset analyzed is relatively small. However, the results seem relatively consistent and the analysis can be repeated with larger data-sets as they become available.<\/jats:p>","DOI":"10.3390\/jcm11082110","type":"journal-article","created":{"date-parts":[[2022,4,10]],"date-time":"2022-04-10T23:06:01Z","timestamp":1649631961000},"page":"2110","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Neural Network Aided Detection of Huntington Disease"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7438-3903","authenticated-orcid":false,"given":"Gerardo","family":"Alfonso Perez","sequence":"first","affiliation":[{"name":"Department of Biochemistry and Molecular Biology, University of Cordoba, 14071 Cordoba, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0571-5147","authenticated-orcid":false,"given":"Javier","family":"Caballero Villarraso","sequence":"additional","affiliation":[{"name":"Department of Biochemistry and Molecular Biology, University of Cordoba, 14071 Cordoba, Spain"},{"name":"Biochemical Laboratory, Reina Sofia University Hospital, 14004 Cordoba, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,10]]},"reference":[{"key":"ref_1","unstructured":"Caron, N.S., Wright, G., and Hayden, M.R. (2022, March 01). Huntington Disease. Gene Reviews. Available online: https:\/\/europepmc.org\/article\/NBK\/nbk1305."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/s13311-013-0244-z","article-title":"Treatment of Huntington\u2019s disease","volume":"11","author":"Frank","year":"2014","journal-title":"Neurotherapeutics"},{"key":"ref_3","first-page":"1","article-title":"Scientific approaches to Huntington\u2019s disease","volume":"1","author":"Sanberg","year":"1984","journal-title":"CRC Crit. Rev. Clin. Neurobiol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1177\/0891988710383573","article-title":"The clinical and genetic features of Huntington disease","volume":"1","author":"Sturrock","year":"2010","journal-title":"J. Geriatr. Psychiatry Neurol."},{"key":"ref_5","first-page":"1","article-title":"Huntington disease","volume":"1","author":"Bates","year":"2015","journal-title":"Nat. Rev. Dis. Prim."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1001\/archneur.58.2.308","article-title":"Huntington disease","volume":"58","author":"Siemers","year":"2001","journal-title":"Arch. Neurol."},{"key":"ref_7","first-page":"1","article-title":"Making (anti-) sense out of huntingtin levels in Huntington disease","volume":"58","author":"Evers","year":"2015","journal-title":"Mol. Neurodegener."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1016\/0888-7543(91)90041-C","article-title":"A gene encoding a fibroblast growth factor receptor isolated from the Huntington disease gene region of human chromosome 4","volume":"11","author":"Thompson","year":"1991","journal-title":"Genomics"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1016\/0888-7543(89)90347-9","article-title":"Segregation of the Huntington disease region of human chromosome 4 in a somatic cell hybrid","volume":"4","author":"Cox","year":"1989","journal-title":"Genomics"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1093\/hmg\/2.7.889","article-title":"Construction of cosmid contigs and high-resolution restriction mapping of the Huntington disease region of human chromosome 4","volume":"2","author":"Zuo","year":"1993","journal-title":"Hum. Mol. Genet."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1038\/nrn1806","article-title":"Normal huntingtin function: An alternative approach to Huntington\u2019s disease","volume":"6","author":"Cattaneo","year":"2005","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1016\/j.neuron.2016.02.003","article-title":"The biology of huntingtin","volume":"89","author":"Saudou","year":"2016","journal-title":"Neuron"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1038\/378398a0","article-title":"A huntingtin-associated protein enriched in brain with implications for pathology","volume":"89","author":"Li","year":"1995","journal-title":"Nature"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1097\/00005072-199805000-00001","article-title":"Huntington disease","volume":"57","author":"Vonsattel","year":"1998","journal-title":"J. Neuropathol. Exp. Neurol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.ncl.2014.09.003","article-title":"Huntington disease: Pathogenesis and treatment","volume":"33","author":"Dayalu","year":"2015","journal-title":"Neurol. Clin."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/B978-0-444-63233-3.00017-8","article-title":"Huntington disease","volume":"147","author":"Ghosh","year":"2018","journal-title":"Handb. Clin. Neurol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1097\/00005792-199709000-00001","article-title":"Huntington disease","volume":"76","author":"Ross","year":"2020","journal-title":"Medicine"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1002\/(SICI)1096-8628(20000103)90:1<49::AID-AJMG10>3.0.CO;2-3","article-title":"Impact of genetic testing for Huntington disease on the family system","volume":"90","author":"Sobel","year":"2000","journal-title":"Am. J. Med. Genet."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1048","DOI":"10.1212\/WNL.49.4.1048","article-title":"Genetic testing of children at risk for Huntington\u2019s disease","volume":"49","author":"Nance","year":"1997","journal-title":"Neurology"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1097\/GIM.0b013e318156e8c1","article-title":"Development of genomic reference materials for Huntington disease genetic testing","volume":"9","author":"Kalman","year":"2007","journal-title":"Genet. Med."},{"key":"ref_21","first-page":"4059","article-title":"DNA methylation","volume":"12","author":"Singal","year":"1999","journal-title":"Blood J. Am. Soc. Hematol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1038\/npp.2012.112","article-title":"DNA methylation","volume":"38","author":"Moore","year":"2013","journal-title":"Neuropsychopharmacology"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1038\/nrg1655","article-title":"DNA methylation and human disease","volume":"6","author":"Robertson","year":"2005","journal-title":"Nat. Rev. Genet."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1146\/annurev.arplant.55.031903.141641","article-title":"DNA methylation and epigenetics","volume":"55","author":"Bender","year":"2004","journal-title":"Annu. Rev. Plant Biol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1098\/rstb.1990.0015","article-title":"DNA methylation and epigenetic inheritance","volume":"326","author":"Holliday","year":"1990","journal-title":"Philos. Trans. R. Soc. Lond. Biol. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1576\/toag.12.1.037.27556","article-title":"DNA methylation: A form of epigenetic control of gene expression","volume":"12","author":"Lim","year":"2010","journal-title":"Obstet. Gynaecol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/S1568-1637(03)00010-2","article-title":"Impact of aging on DNA methylation","volume":"2","author":"Richardson","year":"2003","journal-title":"Ageing Res. Rev."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12915-015-0118-4","article-title":"Aging and DNA methylation","volume":"13","author":"Jung","year":"2015","journal-title":"BMC Biol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-019-1824-y","article-title":"DNA methylation aging clocks: Challenges and recommendations","volume":"20","author":"Bell","year":"2019","journal-title":"Genome Biol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4632","DOI":"10.1200\/JCO.2004.07.151","article-title":"DNA methylation and cancer","volume":"22","author":"Das","year":"2004","journal-title":"J. Clin. Oncol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/B978-0-12-380866-0.60002-2","article-title":"DNA methylation and cancer","volume":"70","author":"Kulis","year":"2010","journal-title":"Adv. Genet."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"s4","DOI":"10.1038\/ncponc0354","article-title":"DNA methylation and gene silencing in cancer","volume":"2","author":"Baylin","year":"2005","journal-title":"Nat. Clin. Pract. Oncol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2025","DOI":"10.1016\/j.neurobiolaging.2008.12.005","article-title":"Epigenetic changes in Alzheimer\u2019s disease: Decrements in DNA methylation","volume":"31","author":"Mastroeni","year":"2010","journal-title":"Neurobiol. Aging"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1078","DOI":"10.1016\/j.bbi.2011.01.017","article-title":"DNA methylation in repetitive elements and Alzheimer disease","volume":"25","author":"Bollati","year":"2011","journal-title":"Brain Behav. Immun."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1156","DOI":"10.1038\/nn.3786","article-title":"Alzheimer\u2019s disease: Early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci","volume":"17","author":"Srivastava","year":"2014","journal-title":"Nat. Neurosci."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Alfonso Perez, G., and Caballero Villarraso, J. (2022). An Entropy Approach to Multiple Sclerosis Identification. J. Pers. Med., 12.","DOI":"10.3390\/jpm12030398"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.fsigen.2017.02.009","article-title":"DNA methylation-based forensic age prediction using artificial neural networks and next generation sequencing","volume":"28","author":"Vidaki","year":"2017","journal-title":"Forensic. Sci. Int. Genet."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/S1525-1578(10)60488-6","article-title":"Classification of individual lung cancer cell lines based on DNA methylation markers: Use of linear discriminant analysis and artificial neural networks","volume":"6","author":"Marchevsky","year":"2004","journal-title":"J. Mol. Diagn."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zheng, C., and Xu, R. (2020). Predicting cancer origins with a DNA methylation-based deep neural network model. PLoS ONE, 5.","DOI":"10.1371\/journal.pone.0226461"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1007\/BF03194647","article-title":"Epigenetic diagnostics of cancer\u2014The application of DNA methylation markers","volume":"47","author":"Paluszczak","year":"2006","journal-title":"J. Appl. Genet."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Liu, B., Liu, Y., Pan, X., Li, M., Yang, S., and Li, S. (2019). DNA methylation markers for pan-cancer prediction by deep learning. Genes, 10.","DOI":"10.3390\/genes10100778"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"101976","DOI":"10.1016\/j.artmed.2020.101976","article-title":"Autoencoded DNA methylation data to predict breast cancer recurrence: Machine learning models and gene-weight significance","volume":"110","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"eaaw8513","DOI":"10.1126\/scitranslmed.aaw8513","article-title":"Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases","volume":"11","author":"Jurmeister","year":"2019","journal-title":"Sci. Transl. Med."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1172\/JCI69740","article-title":"Characterizing DNA methylation alterations from the cancer genome atlas","volume":"124","author":"Paluszczak","year":"2014","journal-title":"J. Clin. Investig."},{"key":"ref_45","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."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Alfonso Perez, G., and Caballero Villarraso, J. (2021). Alzheimer Identification through DNA Methylation and Artificial Intelligence Techniques. Mathematics, 9.","DOI":"10.3390\/math9192482"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.18632\/aging.101005","article-title":"Huntington\u2019s disease accelerates epigenetic aging of human brain and disrupts DNA methylation levels","volume":"8","author":"Horvath","year":"2016","journal-title":"Aging"},{"key":"ref_48","first-page":"2013","article-title":"DNA methylation profiling in human Huntington\u2019s disease brain","volume":"10","author":"Islam","year":"2016","journal-title":"Hum. Mol. Genet."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-020-18255-5","article-title":"DNA methylation study of Huntington\u2019s disease and motor progression in patients and in animal models","volume":"11","author":"Lu","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-017-0811-7","article-title":"A New Evolutionary Algorithm-Based Home Monitoring Device for Parkinson\u2019s Dyskinesia","volume":"41","author":"Lones","year":"2017","journal-title":"J. Med. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1437123","DOI":"10.1155\/2019\/1437123","article-title":"A Classification Algorithm by Combination of Feature Decomposition and Kernel Discriminant Analysis (KDA) for Automatic MR Brain Image Classification and AD Diagnosis","volume":"2019","author":"Elahifasae","year":"2019","journal-title":"Comput. Math. Methods Med."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"102081","DOI":"10.1016\/j.artmed.2021.102081","article-title":"Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques","volume":"117","author":"Tautan","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1753","DOI":"10.3934\/mbe.2021091","article-title":"Artificial intelligence applied to neuroimaging data in Parkinsonian syndromes: Actuality and expectations","volume":"18","author":"Vitale","year":"2021","journal-title":"Math. Biosci. Eng."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Bahado-Sing, R.O., Vishweswaraiah, S., Aydas, B., Mishra, N.K., Guda, C., and Radhakrishna, U. (2019). Deep Learning\/Artificial Intelligence and Blood-Based DNA Epigenomic Prediction of Cerebral Palsy. Int. J. Mol. Sci., 20.","DOI":"10.3390\/ijms20092075"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"768229","DOI":"10.3389\/fnagi.2021.768229","article-title":"Application of Artificial Intelligence Modeling Technology Based on Fluid Biopsy to Diagnose Alzheimer\u2019s Disease","volume":"13","author":"Sh","year":"2021","journal-title":"Front. Aging Neurosci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1016\/j.neuroimage.2009.06.003","article-title":"Automated quantification of caudate atrophy by local registration of serial MRI: Evaluation and application in Huntington\u2019s disease","volume":"47","author":"Tabrizi","year":"2009","journal-title":"Neuroimage"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1016\/j.neuroimage.2010.04.273","article-title":"Evaluating imaging biomarkers for neurodegeneration in pre-symptomatic Huntington\u2019s disease using machine learning techniques","volume":"56","author":"Stoffers","year":"2011","journal-title":"Neuroimage"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2277","DOI":"10.1002\/mds.22477","article-title":"Gene expression changes in blood as a putative biomarker for Huntington\u2019s disease","volume":"25","author":"Lovrecic","year":"2009","journal-title":"Mov. Disord."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1653","DOI":"10.1177\/147323001003800510","article-title":"ADP-ribosylation factor guanine nucleotide-exchange factor 2 (ARFGEF2): A new potential biomarker in Huntington\u2019s disease","volume":"38","author":"Lovrecic","year":"2010","journal-title":"J. Int. Med. Res."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/11\/8\/2110\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:51:24Z","timestamp":1760136684000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/11\/8\/2110"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,10]]},"references-count":59,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["jcm11082110"],"URL":"https:\/\/doi.org\/10.3390\/jcm11082110","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,10]]}}}