A comparative study of clustering methods on gene expression data for lung cancer prognosis.

ABSTRACT: Lung cancer subtyping based on gene expression data is important for identifying patient subgroups with differing survival prognosis to facilitate customized treatment strategies for each subtype of patients. Unsupervised clustering methods are the traditional approach for clustering patients into subtypes. However, since those methods cluster patients based only on gene expression data, the resulting clusters may not always be relevant to the survival outcome of interest. In recent years, semi-supervised and supervised methods have been proposed, which leverage the survival outcome data to identify clusters more relevant to survival prognosis. This paper aims to compare the performance of different clustering methods for identifying clinically prognostic lung cancer subtypes based on two

SUBMITTER: Zhang JZ 

PROVIDER: S-EPMC10630994 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature