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...
Paper Details
Title
Identification of lung cancer gene markers through kernel maximum mean discrepancy and information entropy
Published Date
Dec 1, 2019
Journal
Volume
12
Issue
S8
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