Research article
Accurate molecular classification of cancer using simple rules
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* Corresponding author: Xiaosheng Wang david@genome.ist.i.kyoto-u.ac.jp
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
BMC Medical Genomics 2009, 2:64 doi:10.1186/1755-8794-2-64
Published: 30 October 2009Additional 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
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
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
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
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
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
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
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