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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Zusammenfassung: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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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-019-3158-x