Improved cancer biomarkers identification using network-constrained infinite latent feature selection.

ABSTRACT: Identifying biomarkers that are associated with different types of cancer is an important goal in the field of bioinformatics. Different researcher groups have analyzed the expression profiles of many genes and found some certain genetic patterns that can promote the improvement of targeted therapies, but the significance of some genes is still ambiguous. More reliable and effective biomarkers identification methods are then needed to detect candidate cancer-related genes. In this paper, we proposed a novel method that combines the infinite latent feature selection (ILFS) method with the functional interaction (FIs) network to rank the biomarkers. We applied the proposed method to the expression data of five cancer types. The experiments indicated that our network-constrained ILFS (NCILFS)

SUBMITTER: Cai L 

PROVIDER: S-EPMC7877636 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature