Unsupervised gene selection using biological knowledge : application in sample clustering.

ABSTRACT:

Background

Classification of biological samples of gene expression data is a basic building block in solving several problems in the field of bioinformatics like cancer and other disease diagnosis and making a proper treatment plan. One big challenge in sample classification is handling large dimensional and redundant gene expression data. To reduce the complexity of handling this high dimensional data, gene/feature selection plays a major role.

Results

The current paper explores the use of biological knowledge acquired from Gene Ontology database in selecting the proper subset of genes which can further participate in clustering of samples. The proposed feature selection technique is unsupervised in nature as it does not utilize any class label information in the process of

SUBMITTER: Acharya S 

PROVIDER: S-EPMC5700545 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature