Open AccessCancer classification and biomarker selection via a penalized logsum network-based logistic regression model
Author(s) -
Zhiming Zhou,
Haihui Huang,
Yong Liang
Publication year - 2021
Publication title -
technology and health care
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.281
H-Index - 44
eISSN - 1878-7401
pISSN - 0928-7329
DOI - 10.3233/thc-218026
Subject(s) - logistic regression , gene selection , computer science , selection (genetic algorithm) , feature selection , artificial intelligence , machine learning , model selection , regularization (linguistics) , regression , data mining , statistics , mathematics , gene , biology , microarray analysis techniques , biochemistry , gene expression
In genome research, it is particularly important to identify molecular biomarkers or signaling pathways related to phenotypes. Logistic regression model is a powerful discrimination method that can offer a clear statistical explanation and obtain the classification probability of classification label information. However, it is unable to fulfill biomarker selection.