{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T00:29:06Z","timestamp":1792801746154,"version":"4.5.0"},"reference-count":44,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,10,29]],"date-time":"2022-10-29T00:00:00Z","timestamp":1667001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NIH Ruth L. Kirschstein NRSA T32 Postdoctoral training grant","award":["T32AG044402"],"award-info":[{"award-number":["T32AG044402"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomolecules"],"abstract":"<jats:p>The clinical diagnosis of neurodegenerative diseases is notoriously inaccurate and current methods are often expensive, time-consuming, or invasive. Simple inexpensive and noninvasive methods of diagnosis could provide valuable support for clinicians when combined with cognitive assessment scores. Biological processes leading to neuropathology progress silently for years and are reflected in both the central nervous system and vascular peripheral system. A blood-based screen to distinguish and classify neurodegenerative diseases is especially interesting having low cost, minimal invasiveness, and accessibility to almost any world clinic. In this study, we set out to discover a small set of blood transcripts that can be used to distinguish healthy individuals from those with Alzheimer\u2019s disease, Parkinson\u2019s disease, Huntington\u2019s disease, amyotrophic lateral sclerosis, Friedreich\u2019s ataxia, or frontotemporal dementia. Using existing public datasets, we developed a machine learning algorithm for application on transcripts present in blood and discovered small sets of transcripts that distinguish a number of neurodegenerative diseases with high sensitivity and specificity. We validated the usefulness of blood RNA transcriptomics for the classification of neurodegenerative diseases. Information about features selected for the classification can direct the development of possible treatment strategies.<\/jats:p>","DOI":"10.3390\/biom12111592","type":"journal-article","created":{"date-parts":[[2022,10,29]],"date-time":"2022-10-29T23:45:00Z","timestamp":1667087100000},"page":"1592","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Blood Transcript Biomarkers Selected by Machine Learning Algorithm Classify Neurodegenerative Diseases including Alzheimer\u2019s Disease"],"prefix":"10.3390","volume":"12","author":[{"given":"Carol J.","family":"Huseby","sequence":"first","affiliation":[{"name":"ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ 85281, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elaine","family":"Delvaux","sequence":"additional","affiliation":[{"name":"ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ 85281, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danielle L.","family":"Brokaw","sequence":"additional","affiliation":[{"name":"Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul D.","family":"Coleman","sequence":"additional","affiliation":[{"name":"ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ 85281, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1097\/NEN.0b013e31824b211b","article-title":"Accuracy of the clinical diagnosis of Alzheimer disease at National Institute on Aging Alzheimer Disease Centers, 2005-2010","volume":"71","author":"Beach","year":"2012","journal-title":"J. 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