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Science 15 October 1999: Vol. 286. no. 5439, pp. 531 - 537 DOI: 10.1126/science.286.5439.531
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Reports
Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring
T. R. Golub,
12*
D. K. Slonim,
1
P. Tamayo,
1
C. Huard,
1
M. Gaasenbeek,
1
J. P. Mesirov,
1
H. Coller,
1
M. L. Loh,
2
J. R. Downing,
3
M. A. Caligiuri,
4
C. D. Bloomfield,
4
E. S. Lander
15*
Although cancer classification has improved over the
past 30 years, there has been no general approach for identifying new cancer classes (class discovery) or for assigning tumors to known classes (class prediction). Here, a generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case. A
class discovery procedure automatically discovered the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia
cases. The results demonstrate the feasibility of cancer classification
based solely on gene expression monitoring and suggest a general
strategy for discovering and predicting cancer classes for other types
of cancer, independent of previous biological knowledge.
1 Whitehead Institute/Massachusetts Institute
of Technology Center for Genome Research, Cambridge, MA 02139, USA.
2 Dana-Farber Cancer Institute and Harvard Medical
School, Boston, MA 02115, USA.
3 St. Jude Children's
Research Hospital, Memphis, TN 38105, USA.
4 Comprehensive Cancer Center and Cancer and
Leukemia Group B, Ohio State University, Columbus, OH 43210, USA.
5 Department of Biology, Massachusetts Institute of
Technology, Cambridge, MA 02142, USA.
*
To whom correspondence should be addressed. E-mail:
golub{at}genome.wi.mit.edu; lander{at}genome.wi.mit.edu.
These authors contributed equally to this work.
Read the Full Text
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Bioinformatics
23, 1225-1234
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- A mixture model approach to the tests of concordance and discordance between two large-scale experiments with two-sample groups.
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