High dimensional model representation of log likelihood ratio: binary classification with SNP data

High dimensional model representation of log likelihood ratio: binary classification with SNP data

Abstract Background Developing binary classification rules based on SNP observations has been a major challenge for many modern bioinformatics applications, e.g., predicting risk of future disease events in complex conditions such as cancer. Small-sample, high-dimensional nature of SNP data, weak ef...

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Bibliographic Details
Main Authors: Ali Foroughi pour, Maciej Pietrzak, Lara E. Sucheston-Campbell, Ezgi Karaesmen, Lori A. Dalton, Grzegorz A. Rempała
Format: Article
Language:English
Published: BMC 2020-09-01
Series:BMC Medical Genomics
Subjects:
Single nucleotide polymorphism
Binary classification
High dimensional model representation
Pairwise SNP interactions
Log likelihood ratio
Online Access:http://link.springer.com/article/10.1186/s12920-020-00774-1

Internet

http://link.springer.com/article/10.1186/s12920-020-00774-1

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