Benchmarking machine learning models for late-onset alzheimer’s disease prediction from genomic data

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 grow...

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Veröffentlicht in:	BMC bioinformatics Jg. 20; H. 1; S. 709 - 17
Hauptverfasser:	De Velasco Oriol, Javier, Vallejo, Edgar E., Estrada, Karol, Taméz Peña, José Gerardo, Disease Neuroimaging Initiative, The Alzheimer’s
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 16.12.2019 Springer Nature B.V BMC
Schlagworte:	Accuracy Age Age of Onset Aged Algorithms Alzheimer Disease - genetics Alzheimer's disease Artificial intelligence Benchmarking Bioinformatics Biomarkers Biomedical and Life Sciences Classification Cognitive ability Cohort Studies Computational Biology/Bioinformatics Computer Appl. in Life Sciences Datasets Dementia Dementia disorders Female Genetic diversity Genetic markers Genome-wide association studies Genomics Humans Learning algorithms Life Sciences Machine Learning Machine Learning and Artificial Intelligence in Bioinformatics Machine learning for computational and systems biology Male Medical imaging Methods Microarrays Neurodegenerative diseases Neuroimaging Neuroimaging - methods NMR Nuclear magnetic resonance Predictions Research Article ROC Curve Benchmarking Genome-wide association studies Alzheimer’s disease Machine learning
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
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