Metric learning for DNA microarray data analysis

Shrinkage-based similarity metric for cluster analysis of microarray data.

The current standard correlation coefficient used in the analysis of microarray data was introduced by M. B. Eisen, P. T. Spellman, P. O. Brown, and D. Botstein [(1998) Proc. Natl. Acad. Sci. USA 95, 14863-14868]. Its formulation is rather arbitrary. We give a mathematically rigorous correlation coefficient of two data vectors based on James-Stein shrinkage estimators. We use the assumptions de...

Machine Learning Methods for Microarray Data Analysis

As members of the Dissertation Committee, we certify that we have read the dis-sertation prepared by Prasad Amaresh Gabbur entitled Machine Learning Methods for Microarray Data Analysis and recommend that it be accepted as fulfilling the dissertation requirement for the Degree of Doctor of Philosophy. Final approval and acceptance of this dissertation is contingent upon the candidate's submissi...

A Review of DNA Microarray Data Analysis

GenePublisher: automated analysis of DNA microarray data

GenePublisher, a system for automatic analysis of data from DNA microarray experiments, has been implemented with a web interface at http://www.cbs.dtu.dk/services/GenePublisher. Raw data are uploaded to the server together with a specification of the data. The server performs normalization, statistical analysis and visualization of the data. The results are run against databases of signal tran...

Analysis of DNA microarray expression data.

DNA microarrays are powerful tools for studying biological mechanisms and for developing prognostic and predictive classifiers for identifying the patients who require treatment and are best candidates for specific treatments. Because microarrays produce so much data from each specimen, they offer great opportunities for discovery and great dangers or producing misleading claims. Microarray bas...