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

Summary, in English

The purpose of this study was to develop a method of classifying cancers to specific diagnosticcategories 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 cancersbelong to four distinct diagnostic categories and often present diagnostic dilemmas in clinicalpractice. The ANNs correctly classified all samples and identified the genes most relevant to theclassification. Expression of several of these genes has been reported in SRBCTs, but most havenot been associated with these cancers. To test the ability of the trained ANN models to recognizeSRBCTs, we analyzed additional blinded samples that were not previously used for the trainingprocedure, and correctly classified them in all cases. This study demonstrates the potentialapplications of these methods for tumor diagnosis and the identification of candidate targets fortherapy.

• Author: J Khan; JS Wei; Markus Ringnér; Lao Saal; M Ladanyi; F Westermann; F Berthold; M Schwab; CR Antonescu; Carsten Peterson; PS Meltzer
• Department/s: Computational Biology and Biological Physics - Has been reorganised; Breastcancer-genetics
• Publishing year: 2001
• Language: English
• Pages: 673-679
• Publication/Series: Nature Medicine
• Volume: 7
• Issue: 6
• Links: http://dx.doi.org/10.1038/89044
• Document type: Journal article
• Publisher: Nature Publishing Group
• Topic: Biophysics
• Status: Published
• ISBN/ISSN/Other: ISSN: 1546-170X