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Open Access Highly Accessed Research article

Identifying dysregulated pathways in cancers from pathway interaction networks

Ke-Qin Liu124, Zhi-Ping Liu3, Jin-Kao Hao4*, Luonan Chen135* and Xing-Ming Zhao1*

Author Affiliations

1 Institute of Systems Biology, Shanghai University, Shanghai, 200444, China

2 School of Communication and Information Engineering, Shanghai University, Shanghai, 200072, China

3 Key Laboratory of Systems Biology, SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China

4 LERIA, University of Angers, 2 Boulevard Lavoisier, 49045, Angers Cedex 01, France

5 Institute of Industrial Science, University of Tokyo, Tokyo, 153-8505, Japan

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BMC Bioinformatics 2012, 13:126  doi:10.1186/1471-2105-13-126

Published: 7 June 2012

Additional files

Additional file 1:

Table S1 Classification results on four cancer datasets based on the identified dysregulated pathways. The classification results on four distinct cancer (lung cancer, prostate tumour, breast tumour and pancreatic tumour) datasets by pathway biomarkers, compared with PAC biomarkers, BMI biomarkers and gene biomarkers. (DOC 49 kb)

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Additional file 2:

Table S2 The results of our identified dysregulated pathways on lung cancer test dataset. The results of our identified dysregulated pathways on lung cancer test dataset, compared with PAC biomarkers, BMI biomarkers and gene biomarkers. (DOC 53 kb)

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Additional file 3:

Table S3 The list of identified enriched GO term in dysregulated pathway biomarkers. The list of top 5 enriched GO biological processes of pathway biomarkers in lung cancer (GSE4115) dataset, prostate tumour (GSE6919) and breast tumour (GSE15852) dataset. (XLS 31 kb)

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