Chronic Obstructive Pulmonary Disease: Novel Genes Detection with Penalized Logistic Regression.

ABSTRACT:

Objective

This study aimed to introduce novel techniques for identifying the genes associated with developing chronic obstructive pulmonary disease (COPD) and to prioritize COPD candidate genes using regression methods.

Materials and methods

This is a secondary analysis of the data from an experimental study. We used penalized logistic regressions with three different types of penalties included least absolute shrinkage and selection operator (LASSO), minimax concave penalty (MCP), and smoothly clipped absolute deviation (SCAD). The models were trained using genome-wide expression profiling to define gene networks relevant to the COPD stages. A 10-fold cross-validation scheme was used to evaluate the performance of the methods. In addition, we validate our results by the ext

SUBMITTER: Gohari K 

PROVIDER: S-EPMC10105299 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature