Name
Title | ||
|---|---|---|
An Insight On The 'Large G, Small N' Problem In Gene-Expression Microarray Classification |
Abstract | ||
|---|---|---|
This paper analyzes the effect of the high-dimensional, low-sample size problem in cancer classification using gene-expression microarrays. Here the two key questions addressed are: (i) What is the percentage of genes that can ensure highly accurate classification?, and (ii) Does this percentage differ from one classifier to another? Both these issues are investigated by developing a pool of experiments with two gene ranking algorithms, five classifiers and four DNA microarray databases. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-58838-4_53 | PATTERN RECOGNITION AND IMAGE ANALYSIS (IBPRIA 2017) |
Keywords | Field | DocType |
DNA microarray, Gene expression, Feature ranking, Cancer classification | Cancer classification,Microarray,Gene,Pattern recognition,Computer science,Feature ranking,Gene expression,Artificial intelligence,Gene ranking,Classifier (linguistics),DNA microarray | Conference |
Volume | ISSN | Citations |
10255 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 11 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vicente García | 1 | 78 | 6.37 |
| José Salvador Sánchez | 2 | 184 | 15.36 |
| L. Cleofas-Sánchez | 3 | 12 | 2.51 |
| Humberto de Jesús Ochoa Domínguez | 4 | 0 | 0.68 |
| Francisco López-Orozco | 5 | 0 | 0.34 |