Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
University of California, Berkeley · University of California, San Francisco
Abstract
A reliable and precise classification of tumors is essential for successful diagnosis and treatment of cancer. cDNA microarrays and high-density oligonucleotide chips are novel biotechnologies increasingly used in cancer research. By allowing the monitoring of expression levels in cells for thousands of genes simultaneously, microarray experiments may lead to a more complete understanding of the molecular variations among tumors and hence to a finer and more informative classification. The ability to successfully distinguish between tumor classes (already known or yet to be discovered) using gene expression data is an important aspect of this novel approach to cancer classification. This article compares the…
Citation impact
- FWCI
- 49.12
- Percentile
- 100%
- References
- 35
Authors
3Topics & keywords
- DNA microarray
- Linear discriminant analysis
- Computational biology
- Boosting (machine learning)
- Artificial intelligence
- Computer science
- Gene expression
- Pattern recognition (psychology)
- Reduced inequalities