Benchmarking machine learning models for late-onset alzheimer's disease prediction from genomic data.
BMC Bioinformatics
; 20(1): 709, 2019 Dec 16.
Article
em En
| MEDLINE
| ID: mdl-31842725
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 â¼72% 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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Doença de Alzheimer
Tipo de estudo:
Etiology_studies
/
Incidence_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Aged
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Female
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Humans
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Male
Idioma:
En
Revista:
BMC Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
México
País de publicação:
Reino Unido