An algorithm for finding biologically significant features in microarray data based on a priori manifold learning.

ABSTRACT: Microarray databases are a large source of genetic data, which, upon proper analysis, could enhance our understanding of biology and medicine. Many microarray experiments have been designed to investigate the genetic mechanisms of cancer, and analytical approaches have been applied in order to classify different types of cancer or distinguish between cancerous and non-cancerous tissue. However, microarrays are high-dimensional datasets with high levels of noise and this causes problems when using machine learning methods. A popular approach to this problem is to search for a set of features that will simplify the structure and to some degree remove the noise from the data. The most widely used approach to feature extraction is principal component analysis (PCA) which assumes a multivariate

SUBMITTER: Hira ZM 

PROVIDER: S-EPMC3940899 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature