ArticleJournal of the American Medical Informatics Association : JAMIA
The application of naive Bayes model averaging to predict Alzheimer's disease from genome-wide data.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed, 109 citations in OpenAlex.
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- Recent Advances in the Application of Artificial Intelligence in Alzheimer's Disease.Current Alzheimer research · 2025Review
- Integrating Demographics and Imaging Features for Various Stages of Dementia Classification: Feed Forward Neural Network Multi-Class Approach.Biomedicines · 2024Article
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- Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.Diagnostics (Basel, Switzerland) · 2022Article
- Autism screening: an unsupervised machine learning approach.Health information science and systems · 2022Article
- Artificial intelligence and machine learning in precision and genomic medicine.Medical oncology (Northwood, London, England) · 2022Review
- Artificial intelligence, machine learning, and deep learning for clinical outcome prediction.Emerging topics in life sciences · 2021Article
- Bayesian Network as a Decision Tool for Predicting ALS Disease.Brain sciences · 2021Article
- Machine learning for the life-time risk prediction of Alzheimer's disease: a systematic review.Brain communications · 2021Review
- Comparison of machine learning methods for estimating case fatality ratios: An Ebola outbreak simulation study.PloS one · 2021Article
- The Application of Artificial Intelligence in the Genetic Study of Alzheimer's Disease.Aging and disease · 2020Review
- Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine.Database : the journal of biological databases and curation · 2020Article
- The Role of Genetic Factors in Characterizing Extra-Intestinal Manifestations in Crohn's Disease Patients: Are Bayesian Machine Learning Methods Improving Outcome Predictions?Journal of clinical medicine · 2019Article
- Applying Naive Bayesian Networks to Disease Prediction: a Systematic Review.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2016Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
objectivePredicting patient outcomes from genome-wide measurements holds significant promise for improving clinical care. The large number of measurements (eg, single nucleotide polymorphisms (SNPs)), however, makes this task computationally challenging. This paper evaluates the performance of an algorithm that predicts patient outcomes from genome-wide data by efficiently model averaging over an exponential number of naive Bayes (NB) models.
designThis model-averaged naive Bayes (MANB) method was applied to predict late onset Alzheimer's disease in 1411 individuals who each had 312,318 SNP measurements available as genome-wide predictive features. Its performance was compared to that of a naive Bayes algorithm without feature selection (NB) and with feature selection (FSNB). MEASUREMENT: Performance of each algorithm was measured in terms of area under the ROC curve (AUC), calibration, and run time.
resultsThe training time of MANB (16.1 s) was fast like NB (15.6 s), while FSNB (1684.2 s) was considerably slower. Each of the three algorithms required less than 0.1 s to predict the outcome of a test case. MANB had an AUC of 0.72, which is significantly better than the AUC of 0.59 by NB (p<0.00001), but not significantly different from the AUC of 0.71 by FSNB. MANB was better calibrated than NB, and FSNB was even better in calibration. A limitation was that only one dataset and two comparison algorithms were included in this study.
conclusionMANB performed comparatively well in predicting a clinical outcome from a high-dimensional genome-wide dataset. These results provide support for including MANB in the methods used to predict outcomes from large, genome-wide datasets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.