In-silico drug screening and potential target identification for hepatocellular carcinoma using Support Vector Machines based on drug screening result |
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| Authors: | Wu-Lung R. Yang Yu-En Lee Ming-Huang Chen Kun-Mao Chao Chi-Ying F. Huang |
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| Affiliation: | 1. Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan;2. Institute of Biotechnology in Medicine, National Yang-Ming University, Taipei, Taiwan;3. Institute 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 Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan;6. Institute of Biopharmaceutical Sciences, National Yang-Ming University, Taipei, Taiwan |
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| Abstract: | Hepatocellular carcinoma (HCC) is a severe liver malignancy with few drug treatment options. In finding an effective treatment for HCC, screening drugs that are already FDA-approved will fast track the clinical trial and drug approval process. Connectivity Map (CMap), a large repository of chemical-induced gene expression profiles, provides the opportunity to analyze drug properties on the basis of gene expression. Support Vector Machines (SVM) were utilized to classify the effectiveness of drugs against HCC using gene expression profiles in CMap. The results of this classification will help us (1) identify genes that are chemically sensitive, and (2) predict the effectiveness of remaining chemicals in CMap in the treatment of HCC and provide a prioritized list of possible HCC drugs for biological verification. |
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| Keywords: | HCC, hepatocellular carcinoma CMap, Connectivity Map MTT, 3-(4,5-cimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide IC50, concentration required to inhibit cell growth by 50% |
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