Support Vector Machine Classification of Microarray Data (1998)
| Venue: | AI Memo 1677, Massachusetts Institute of Technology |
| Citations: | 40 - 6 self |
BibTeX
@TECHREPORT{Mukherjee98supportvector,
author = {S. Mukherjee and P. Tamayo and D. Slonim and A. Verri and T. Golub and J. P. Mesirov and T. Poggio},
title = {Support Vector Machine Classification of Microarray Data},
institution = {AI Memo 1677, Massachusetts Institute of Technology},
year = {1998}
}
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Abstract
An effective approach to cancer classification based upon gene expression monitoring using DNA microarrays was introduced by Golub et. al. [3]. The main problem they faced was accurately assigning leukemia samples the class labels acute myeloid leukemia (AML) or acute lymphoblastic leukemia (ALL). We used a Support Vector Machine (SVM) classifier to assign these labels. The motivation for the use of a SVM is that DNA microarray problems can be very high dimensional and have very few training data. This type of situation is particularly well suited for an SVM approach. We achieve slightly better performance on this (simple) classification task than Golub et. al. Copyright c fl Massachusetts Institute of Technology, 1998 This report describes research done within the Center for Biological and Computational Learning in the Department of Brain and Cognitive Sciences and at the Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. This research is sponsor...
Citations
| 216 | T.: A general framework for object detection - PAPAGEORGIOU, OREN, et al. - 1998 |
| 52 | Geometry and invariance in kernel based methods - Burges - 1999 |
| 5 | Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring - Loh, Caligiuri, et al. - 1999 |
| 4 | Multivariate density estimation: An svm approach - Mukherjee, Vapnik - 1999 |
