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

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

Abstract Background Binary classification rules based on a small-sample of high-dimensional data (for instance, gene expression data) are ubiquitous in modern bioinformatics. Constructing such classifiers is challenging due to (a) the complex nature of underlying biological traits, such as gene inte...

Full description

Bibliographic Details
Main Authors: Ali Foroughi pour, Maciej Pietrzak, Lori A Dalton, Grzegorz A. Rempała
Format: Article
Language:English
Published: BMC 2020-04-01
Series:BMC Bioinformatics
Subjects:
High dimensional model representation
Classification
Disease prediction
Log-likelihood ratio
Expression analysis
Online Access:http://link.springer.com/article/10.1186/s12859-020-3486-x

Internet

http://link.springer.com/article/10.1186/s12859-020-3486-x

Similar Items