[A signature based on relative gene expression orderings for lung cancer diagnosis].

ABSTRACT: Traditional classifiers, such as support vector machine and Bayesian classifier, require data normalization for removing experimental batch effects, which limit their applications at the individual level. In this paper,we aim to build a classifier to distinguish lung cancer and non-cancer lung tissues(pneumonia and normal lung tissues).We identified gene pairs as signatures to build a classifier based on the within-sample relative expression orderings of gene pairs in a particular type of tissues(cancer or non-cancer). Using multiple independent datasets as the training data,including a total of 197 lung cancer cases and 189 non-cancer cases, we identified three gene pairs. Classifying a sample by the majority voting rule, the average accuracy reached 95.34% in the training data. Using mul

SUBMITTER: Chen Y 

PROVIDER: S-EPMC9935376 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature