Multiclass prediction with partial least square regression for gene expression data: applications in breast cancer intrinsic taxonomy.

ABSTRACT: Multiclass prediction remains an obstacle for high-throughput data analysis such as microarray gene expression profiles. Despite recent advancements in machine learning and bioinformatics, most classification tools were limited to the applications of binary responses. Our aim was to apply partial least square (PLS) regression for breast cancer intrinsic taxonomy, of which five distinct molecular subtypes were identified. The PAM50 signature genes were used as predictive variables in PLS analysis, and the latent gene component scores were used in binary logistic regression for each molecular subtype. The 139 prototypical arrays for PAM50 development were used as training dataset, and three independent microarray studies with Han Chinese origin were used for independent validation (n = 535).

SUBMITTER: Huang CC 

PROVIDER: S-EPMC3893734 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature