Naïve Bayesian Classifier and Genetic Risk Score for Genetic Risk Prediction of a Categorical Trait: Not so Different after all!

By Paola Sebastiani, N. Solovieff, Jenny X. Sun. 2012

57 citations recorded by the source corpus.

One of the most popular modeling approaches to genetic risk prediction is to use a summary of risk alleles in the form of an unweighted or a weighted genetic risk score, with weights that relate to the odds for the phenotype in carriers of the individual alleles. Recent contributions have proposed the use of Bayesian classification rules using Naïve Bayes classifiers. We examine the relation between the two approaches for genetic risk prediction and show that the methods are mathematically related. In addition, we study the properties of the two approaches and describe how they can be generalized to include various models of inheritance.

Subfields: Epidemiology, Genetics

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In Genetics

1. Arturo Lopez Pineda
2. Gregory F. Cooper
3. Shyam Visweswaran
4. F. Tsui
5. Michael M. Wagner
6. Ye Ye

Citations in the corpus

• Natural Language Processing for EHR-Based Computational Phenotyping (2018, neutral): “Some studies have suggested that results obtained from logistic regression and Naive Bayes are comparable [93].”
• Comparison of Machine Learning Classifiers for Influenza Detection from Emergency Department Free-text Reports (2015, methodology): “It has been suggested that the predictions obtained from a LR model are the same as those predictions originated from a NB model [46].”