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Article
Nature Medicine  7, 673 - 679 (2001)
doi:10.1038/89044

Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks

Javed Khan1, 2, 7, Jun S. Wei1, 7, Markus Ringnér1, 3, 7, Lao H. Saal1, Marc Ladanyi4, Frank Westermann5, Frank Berthold6, Manfred Schwab5, Cristina R. Antonescu4, Carsten Peterson3 & Paul S. Meltzer1

1  Cancer Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA

2  Pediatric Oncology Branch, Advanced Technology Center, National Cancer Institute, Gaithersburg, Maryland, USA

3  Complex Systems Division, Department of Theoretical Physics, Lund University, Lund, Sweden

4  Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, New York, USA

5  Department of Cytogenetics, German Cancer Research Center, Heidelberg, Germany

6  Department of Pediatrics, Klinik für Kinderheilkunde der Universität zu Köln, Köln, Germany

7  J.K., J.S.W. and M.R. contributed equally to this study.

Correspondence should be addressed to Javed Khan khanjav@mail.nih.gov or Paul S. Meltzer pmeltzer@nhgri.nih.gov
The purpose of this study was to develop a method of classifying cancers to specific diagnostic categories based on their gene expression signatures using artificial neural networks (ANNs). We trained the ANNs using the small, round blue-cell tumors (SRBCTs) as a model. These cancers belong to four distinct diagnostic categories and often present diagnostic dilemmas in clinical practice. The ANNs correctly classified all samples and identified the genes most relevant to the classification. Expression of several of these genes has been reported in SRBCTs, but most have not been associated with these cancers. To test the ability of the trained ANN models to recognize SRBCTs, we analyzed additional blinded samples that were not previously used for the training procedure, and correctly classified them in all cases. This study demonstrates the potential applications of these methods for tumor diagnosis and the identification of candidate targets for therapy.

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Nature Medicine
ISSN: 1078-8956
EISSN: 1546-170X
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