|Publication Type||Journal Article|
|Year of Publication||2001|
|Authors||Yeang, CH, Ramaswamy, S, Tamayo, P, Mukherjee, S, Rifkin, RM, Angelo, M, Reich, M, Lander, E, Mesirov, J, Golub, T|
|Journal||Bioinformatics (Oxford, England)|
|Volume||17 Suppl 1|
|Pages||S316-22 - S316-22|
|Keywords||Algorithms, Cancer, Computational Biology, Confidence Intervals, Databases, Gene Expression Profiling, Genetic, Humans, Neoplasms|
Using gene expression data to classify tumor types is a very promising tool in cancer diagnosis. Previous works show several pairs of tumor types can be successfully distinguished by their gene expression patterns (Golub et al. 1999, Ben-Dor et al. 2000, Alizadeh et al. 2000). However, the simultaneous classification across a heterogeneous set of tumor types has not been well studied yet. We obtained 190 samples from 14 tumor classes and generated a combined expression dataset containing 16063 genes for each of those samples. We performed multi-class classification by combining the outputs of binary classifiers. Three binary classifiers (k-nearest neighbors, weighted voting, and support vector machines) were applied in conjunction with three combination scenarios (one-vs-all, all-pairs, hierarchical partitioning). We achieved the best cross validation error rate of 18.75% and the best test error rate of 21.74% by using the one-vs-all support vector machine algorithm. The results demonstrate the feasibility of performing clinically useful classification from samples of multiple tumor types.