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An efficient bayesian method for predicting clinical outcomes from genome-wide data.

Year: 2010

Type: article

Abstract: This paper compares the predictive performance and efficiency of several machine-learning methods when applied to a genome-wide dataset on Alzheimer's disease that contains 312,318 SNP measurements on 1411 cases. In particular, a Bayesian algorithm is introduced and compared to several standard machine-learning methods. The results show that the Bayesian algorithm predicts outcomes comparably to the standard methods, and it requires less total training time. These results support the further development and evaluation of the Bayesian algorithm.

Source: PubMed Central

Authors: Gregory F Cooper, Pablo Hennings-Yeomans, Shyam Visweswaran, Michael Barmada

Language: English

Topic: Genetic Associations and Epidemiology

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

Funder: U.S. National Library of Medicine

Award: Predicting Patient Outcomes from Clinical and Genome-Wide Data

OpenAlex ID: W1877160699

FWCI: 0.973

Cites: 10

Cited by: 23

Related to: 10