Molecular classification of multiple tumor types.
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.
Yeang
C H
CH
Center for Genome Research, MIT Whitehead Institute, One Kendall Square, Cambridge, MA 02139, USA. chyeang@mit.edu
Ramaswamy
S
S
Tamayo
P
P
Mukherjee
S
S
Rifkin
R M
RM
Angelo
M
M
Reich
M
M
Lander
E
E
Mesirov
J
J
Golub
T
T
D000465:Algorithms; D019295:Computational Biology; D016001:Confidence Intervals; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D006801:Humans; D009369:Neoplasms / Q000145:classification* / Q000235:genetics*