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This article is part of the supplement: Beyond the Genome 2012

Open Access Poster presentation

In silico drug screening and potential target identification for hepatocellular carcinoma using support vector machine

Wu-Lung R Yang1, Yu-En Lee2, Ming-Huang Chen3, Yu-Wen Liu3, Pei-Ying Lee3, Kun-Mao Chao145 and Chi-Ying F Huang36*

  • * Corresponding author: Chi-Ying F Huang

Author Affiliations

1 Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan

2 lnstitute of Biotechnology in Medicine, National Yang-Ming University, Taipei, Taiwan

3 lnstitute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan

4 Division of Hematology and Oncology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan

5 Graduate Institute of Biomedical Electronic and Bioinformatics, National Taiwan University, Taipei, Taiwan

6 Institute of Biopharmaceutical Sciences, National Yang-Ming University, Taipei, Taiwan

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BMC Proceedings 2012, 6(Suppl 6):P16  doi:10.1186/1753-6561-6-S6-P16


The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1753-6561/6/S6/P16


Published:1 October 2012

© 2012 Yang et al; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Poster presentation

Hepatocellular carcinoma (HCC) is a severe liver malignancy with few drug treatment options. Drug screening using FDA-approved drugs will provide a fast track in clinical trials if drugs are found effective against HCC. The Connectivity Map (cmap), a large repository of chemical-induced gene expression profiles, provides the opportunity of analyzing drug property with the expression. A support vector machine (SVM) was utilized to classify the effectiveness of drugs against HCC using gene expression profiles in cmap. The classification results will help us to identify significant chemical-sensitivity genes, and to predict the effectiveness of remaining chemicals in cmap, with a prioritized listing for biological verification. The cell viability of four HCC cell lines treated with 146 chemicals was conducted. The SVM successfully classified the effectiveness of chemicals with an average area under the receiver operating curve of 0.9. Chemical sensitivity genes which are possible HCC therapeutic targets, such as MT1E, MYC and GADD45B, were identified with opposite signs of gene differential changes compared with reported HCC patient samples. Several known HCC inhibitors, such as geldanamycin, alvespimycin (histone deacetylase inhibitors) and doxorubicin (chemotherapy drug), were predicted to be effective. Seven out of 23 predicted drugs were cardiac glycosides, suggesting a close link of these drugs to the inhibition of HCC. The study demonstrates a strategy of in silico drug screening using a large repository of microarrays based on initial in vitro drug screening results. The biological verification result can serve as a feedback into the process for the development of a more accurate chemical sensitivity model.