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Open Access Highly Accessed Research article

Accurate molecular classification of cancer using simple rules

Xiaosheng Wang1* and Osamu Gotoh12

Author Affiliations

1 Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan

2 National Institute of Advanced Industrial Science and Technology, Computational Biology Research Center, Tokyo 135-0064, Japan

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BMC Medical Genomics 2009, 2:64  doi:10.1186/1755-8794-2-64

Published: 30 October 2009

Additional files

Additional file 1:

The rules derived from each of the 12 gene pairs identified in the Leukemia dataset 1.

Format: TXT Size: 2KB Download file

Open Data

Additional file 2:

The top 87 genes with depended degrees of no less than 0.5 in the training set of the Leukemia dataset 1.

Format: XLS Size: 27KB Download file

This file can be viewed with: Microsoft Excel Viewer

Open Data

Additional file 3:

The rules produced by each of the 16 genes and 25 gene pairs identified in the Lung Cancer dataset.

Format: TXT Size: 7KB Download file

Open Data

Additional file 4:

The experimental results and the seven gene pairs with high classification accuracy in the test set of the Lung Cancer dataset, identified without LOOCV.

Format: DOC Size: 40KB Download file

This file can be viewed with: Microsoft Word Viewer

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Additional file 5:

The classification rules generated by each of the eight genes and three gene pairs identified in the Prostate Cancer dataset.

Format: TXT Size: 2KB Download file

Open Data

Additional file 6:

The top 20 genes ranked based on depended degree in the training set of the Prostate Cancer dataset.

Format: XLS Size: 16KB Download file

This file can be viewed with: Microsoft Excel Viewer

Open Data

Additional file 7:

The classification rules generated by each of the eight genes identified in the Breast Cancer dataset.

Format: TXT Size: 1KB Download file

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Additional file 8:

The classification rules generated by each of the 21 genes identified in the Leukemia dataset 2.

Format: TXT Size: 4KB Download file

Open Data