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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Vydáno v:	BMC bioinformatics Ročník 20; číslo 1; s. 709 - 17
Hlavní autoři:	De Velasco Oriol, Javier, Vallejo, Edgar E., Estrada, Karol, Taméz Peña, José Gerardo, Disease Neuroimaging Initiative, The Alzheimer’s
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 16.12.2019 Springer Nature B.V BMC
Témata:	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
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1471-2105 1471-2105
DOI:	10.1186/s12859-019-3158-x