Multi-resolution independent component analysis for high-performance tumor classification and biomarker discovery.

ABSTRACT:

Background

Although high-throughput microarray based molecular diagnostic technologies show a great promise in cancer diagnosis, it is still far from a clinical application due to its low and instable sensitivities and specificities in cancer molecular pattern recognition. In fact, high-dimensional and heterogeneous tumor profiles challenge current machine learning methodologies for its small number of samples and large or even huge number of variables (genes). This naturally calls for the use of an effective feature selection in microarray data classification.

Methods

We propose a novel feature selection method: multi-resolution independent component analysis (MICA) for large-scale gene expression data. This method overcomes the weak points of the widely used transform-base

SUBMITTER: Han H 

PROVIDER: S-EPMC3044315 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature