2001 MulticlassCancerDiagUsingTumorGeneExpr
Jump to navigation
Jump to search
- (Ramswamy et al., 2001) ⇒ Sridhar Ramaswamy, Pablo Tamayo, Ryan Rifkin, Sayan Mukherjee, Chen-Hsiang Yeang, Michael Angelo, Christine Ladd, Michael Reich, Eva Latulippe, Jill P. Mesirov, Tomaso Poggio, William Gerald, Massimo Loda, Eric S. Lander, Todd R. Golub. (2001). “Multiclass Cancer Diagnosis Using Tumor Gene Expression Signatures.” In: Proceedings of the National Academy of Sciences of the United States of America (PNAS), 98(26) doi:10.1073/pnas.211566398
Subject Headings: Multiclass Classification Task.
Notes
Cited By
~1070 http://scholar.google.com/scholar?cites=3873526504974074566
Quotes
Abstract
- The optimal treatment of patients with cancer depends on establishing accurate diagnoses by using a complex combination of clinical and histopathological data. In some instances, this task is difficult or impossible because of atypical clinical presentation or histopathology. To determine whether the diagnosis of multiple common adult malignancies could be achieved purely by molecular classification, we subjected 218 tumor samples, spanning 14 common tumor types, and 90 normal tissue samples to oligonucleotide microarray gene expression analysis. The expression levels of 16,063 genes and expressed sequence tags were used to evaluate the accuracy of a multiclass classifier based on a support vector machine algorithm. Overall classification accuracy was 78%, far exceeding the accuracy of random classification (9%). Poorly differentiated cancers resulted in low-confidence predictions and could not be accurately classified according to their tissue of origin, indicating that they are molecularly distinct entities with dramatically different gene expression patterns compared with their well differentiated counterparts. Taken together, these results demonstrate the feasibility of accurate, multiclass molecular cancer classification and suggest a strategy for future clinical implementation of molecular cancer diagnostics.
Support Vector Machine (SVM) Algorithm and One vs. All (OVA) Classification Scheme.
- … In going from binary to multiclass classification, we used an OVA approach (described in Results). Given m classes and m trained classifier)s, a new sample takes the class of the classifier with the largest real valued output class = arg maxi=1...m fi, where f i is the real valued output of the ith classifier. A positive prediction strength corresponds to a test sample being assigned to a single class rather than to the “all other” class.
References
,