| Title | Improved gene selection for classification of microarrays. |
| Publication Type | Journal Article |
| Year of Publication | 2003 |
| Authors | Jaeger J, Sengupta R, Ruzzo WL |
| Journal | Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing |
| Pagination | 53-64 |
| Date or Month Published | 2003 |
| ISSN | 1793-5091 |
| Keywords | Algorithms, Cluster Analysis, Databases, Genetic, Gene Expression Profiling, Humans, Neoplasms, Oligonucleotide Array Sequence Analysis |
| Abstract | In this paper we derive a method for evaluating and improving techniques for selecting informative genes from microarray data. Genes of interest are typically selected by ranking genes according to a test-statistic and then choosing the top k genes. A problem with this approach is that many of these genes are highly correlated. For classification purposes it would be ideal to have distinct but still highly informative genes. We propose three different pre-filter methods--two based on clustering and one based on correlation--to retrieve groups of similar genes. For these groups we apply a test-statistic to finally select genes of interest. We show that this filtered set of genes can be used to significantly improve existing classifiers. |
| Downloads | http://www.ncbi.nlm.nih.gov/pubmed/12603017?dopt=Abstract |
| Alternate Journal | Pac Symp Biocomput |
| Citation Key | 1888 |
| PubMed ID | 12603017 |
