Identification of lung cancer gene markers through kernel maximum mean discrepancy and information entropy.

ABSTRACT:

Background

The early diagnosis of lung cancer has been a critical problem in clinical practice for a long time and identifying differentially expressed gene as disease marker is a promising solution. However, the most existing gene differential expression analysis (DEA) methods have two main drawbacks: First, these methods are based on fixed statistical hypotheses and not always effective; Second, these methods can not identify a certain expression level boundary when there is no obvious expression level gap between control and experiment groups.

Methods

This paper proposed a novel approach to identify marker genes and gene expression level boundary for lung cancer. By calculating a kernel maximum mean discrepancy, our method can evaluate the expression differences between n

SUBMITTER: Zhao Z 

PROVIDER: S-EPMC6923882 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature