Name
Abstract | ||
|---|---|---|
Array-based comparative genomic hybridization (arrayCGH) has recently become a popular tool to identify DNA copy number variations along the genome. These profiles are starting to be used as markers to improve prognosis or diagnosis of cancer, which implies that methods for automated supervised classification of arrayCGH data are needed. Like gene expression profiles, arrayCGH profiles are characterized by a large number of variables usually measured on a limited number of samples. However, arrayCGH profiles have a particular structure of correlations between variables, due to the spatial organization of bacterial artificial chromosomes along the genome. This suggests that classical classification methods, often based on the selection of a small number of discriminative features, may not be the most accurate methods and may not produce easily interpretable prediction rules.We propose a new method for supervised classification of arrayCGH data. The method is a variant of support vector machine that incorporates the biological specificities of DNA copy number variations along the genome as prior knowledge. The resulting classifier is a sparse linear classifier based on a limited number of regions automatically selected on the chromosomes, leading to easy interpretation and identification of discriminative regions of the genome. We test this method on three classification problems for bladder and uveal cancer, involving both diagnosis and prognosis. We demonstrate that the introduction of the new prior on the classifier leads not only to more accurate predictions, but also to the identification of known and new regions of interest in the genome.All data and algorithms are publicly available. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1093/bioinformatics/btn188 | ISMB |
Keywords | Field | DocType |
arraycgh profile,automated supervised classification,small number,dna copy number variation,large number,supervised classification,classification problem,classical classification method,fused svm,arraycgh data,limited number,spatial organization,support vector machine,bacterial artificial chromosome,region of interest | Genome,Small number,Data mining,Computer science,Comparative genomic hybridization,Artificial intelligence,Classifier (linguistics),Discriminative model,Pattern recognition,Copy-number variation,Support vector machine,Bioinformatics,Linear classifier | Conference |
Volume | Issue | ISSN |
24 | 13 | 1367-4811 |
Citations | PageRank | References |
28 | 1.80 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Franck Rapaport | 1 | 121 | 7.41 |
| Emmanuel Barillot | 2 | 950 | 165.00 |
| Jean-philippe Vert | 3 | 2754 | 158.52 |