Multi-view based integrative analysis of gene expression data for identifying biomarkers.

ABSTRACT: The widespread applications in microarray technology have produced the vast quantity of publicly available gene expression datasets. However, analysis of gene expression data using biostatistics and machine learning approaches is a challenging task due to (1) high noise; (2) small sample size with high dimensionality; (3) batch effects and (4) low reproducibility of significant biomarkers. These issues reveal the complexity of gene expression data, thus significantly obstructing microarray technology in clinical applications. The integrative analysis offers an opportunity to address these issues and provides a more comprehensive understanding of the biological systems, but current methods have several limitations. This work leverages state of the art machine learning development for multip

SUBMITTER: Yang ZY 

PROVIDER: S-EPMC6751173 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature