Benchmarking machine learning models for late-onset alzheimer’s disease prediction from genomic data
Open accessAbstract
Background: Late-Onset Alzheimer’s Disease (LOAD) is a leading form of dementia. There is no effective cure for LOAD, leaving the treatment efforts to depend on preventive cognitive therapies, which stand to benefit from the timely estimation of the risk of developing the disease. Fortunately, a growing number of Machine Learning methods that are well positioned to address this challenge are becoming available. Results: We conducted systematic comparisons of representative Machine Learning models for predicting LOAD from genetic variation data provided by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Our experimental results demonstrate that the classification performance of the best models tested yielded ∼72% of area under the ROC curve. Conclusions: Machine learning models are promising alternatives for estimating the genetic risk of LOAD. Systematic machine learning model selection also provides the opportunity to identify new genetic markers potentially associated with the disease.
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Javier et al. achieved 0.719 accuracy on ADNI using genetic variation data.
“| Javier et al. [87] | genetic variation data | Gene-based | ADNI | 0.719 (ACC) |”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [87]
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A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis
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- Authors
- De Velasco Oriol, J., Vallejo, E. E., Estrada, K., Taméz Peña, J. G., & Disease Neuroimaging Initiative, T. A.
- Affiliations
- Tecnológico de Monterrey (MX)
- Brandeis University (US)
- Venue
- BMC Bioinformatics
- Published
- 2019-12
- Volume
- 20
- Issue
- 1
- Pages
- 709-709
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC6915925
- OpenAlex
- W2995918088
- Cited by
- 71
- References
- 30
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 11 · 2023: 14 · 2022: 13 · 2021: 15 · 2020: 3
- FWCI
- 6.17
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- U.S. Department of Defense (U01 AG024904, W81XWH, W81XWH-12-2-, W81XWH1220012, AG024904)
- Foundation for the National Institutes of Health (W81XWH-12-2-0012, U01AG024904, Grant U01 AG024904, U24 AG021886, AG024904, U01 AG016976, N01-AG-12100)
- Alzheimer's Association (U01 AG016976, U01 AG032984, U01 AG024904, U24AG021886, AG033193, W81XWH-12-2-0012)
- Bristol-Myers Squibb (U01 AG024904, AG024904, W81XWH-12-2-0012)
- Eli Lilly and Company (U01 AG024904, AG024904, W81XWH-12-2-0012)
- Pfizer (AG024904, W81XWH-12-2-0012, U01 AG024904)
- Biogen (W81XWH-12-2-0012, U01 AG024904, AG024904)
- University of Southern California (W81XWH-12-2-0012, U01 AG024904)
- F. Hoffmann-La Roche (W81XWH-12-2-0012, AG024904, U01 AG024904)
- Meso Scale Diagnostics (U01 AG024904, AG024904, W81XWH-12-2-0012)
- Alzheimer's Disease Neuroimaging Initiative (W81XWH-12-2–0012, U01 AG032984, U01AG024904, AG024904, W81XWH-12-2-, Department of Defense award number W81XWH-12-2-0012)
- BioClinica (U01 AG024904, W81XWH-12-2-0012, AG024904)
- Novartis Pharmaceuticals Corporation (W81XWH-12-2-0012, U01 AG024904, AG024904)
- Northern California Institute for Research and Education (U01 AG024904, W81XWH-12-2-0012)
- Wellcome Trust (082604/2/07/Z, 082604)
- Development of Innovative Strategies for a Transdisciplinary approach to ALZheimer's disease
- Université de Lille
- Institut National de la Santé et de la Recherche Médicale
- Bundesministerium für Bildung und Forschung (01GI0102, 01GI0711, 01GI0420)
- Erasmus Medisch Centrum
- Consejo Nacional de Ciencia y Tecnología (U01 AG024904, W81XWH-12-2-0012)
- Eisai (W81XWH-12-2-0012, U01 AG024904, AG024904)
- Servier (AG024904, W81XWH-12-2-0012, U01 AG024904)
- Hjartavernd (N01-AG-1-2100)
- H. Lundbeck A/S (U01 AG024904, AG024904, W81XWH-12-2-0012)
- Centre hospitalier régional universitaire de Lille
- IXICO (AG024904, W81XWH-12-2-0012, U01 AG024904)
- National Institutes of Health (3u01ag024904-10s2, N01-AG-1-2100, W81XWH, W81XWH-12-2-0012, NIA AG081220, AG016976, U01-AG024904, Grant U01 AG024904, AG024904, AG021886)
- Genentech (W81XWH-12-2-0012, AG024904, U01 AG024904)
- Canadian Institutes of Health Research (U01 AG024904, W81XWH-12-2-0012)
- Medical Research Council (503480, U01 AG024904)
- National Institute on Aging (U01-AG-024904, AG 016976, W81XWH-12-2–0012, N01-AG12100, AG 024904, AG081220, U01AG016976, NIA AG081220, U01AG032984, AG033193, AG032984, R01AG033193, AG021886, U24AG021886)
- National Heart, Lung, and Blood Institute (HL-105756, R01HL105756, AG033193)
- National Institute of Biomedical Imaging and Bioengineering (W81XWH-12-2-0012, U01 AG024904, AG024904)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- late-onset Alzheimer's disease, disease risk prediction, genetic risk, genomic data, classifier comparison, genetic markers, ADNI cohort, dementia prevention
- MeSH terms
- Machine Learning, Aged, Alzheimer Disease, Female, Humans, Male, ROC Curve, Cohort Studies, Age of Onset, Benchmarking, Genomics, Neuroimaging
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