| 研究課題/領域番号 |
23300105
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| 研究機関 | 北陸先端科学技術大学院大学 |
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研究代表者 |
TU・BAO Ho 北陸先端科学技術大学院大学, 知識科学研究科, 教授 (60301199)
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| 研究分担者 |
神田 達郎 千葉大学, 医学(系)研究科(研究院), 講師 (20345002)
河崎 さおり 北陸先端科学技術大学院大学, 先端領域社会人教育院, 特任准教授 (40377437)
DAM HieuChi 北陸先端科学技術大学院大学, 知識科学研究科, 准教授 (70397230)
横須賀 收 千葉大学, 医学(系)研究科(研究院), 教授 (90182691)
高林 克日己 千葉大学, 医学部附属病院, 教授 (90188079)
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| 研究期間 (年度) |
2011-04-01 – 2014-03-31
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| キーワード | data mining / machine learning / hepatitis C virus / epigenetics / siRNA / hepatitis pathogenesis |
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| 研究概要 |
In this period of the research project we aimed at establishing a calculation approach based on techniques of data mining and machine learning for various biomedical data sources. Concretely, we work to contribute to the studies of the molecular mechanisms in the treatment and pathogenesis of hepatitis: (1) the resistance mechanisms of hepatitis C virus to the standard treatment by combination of interferon (IFN) and ribavirin (RBV) for therapy (HCV) in the NS5A protein, (2) the interaction of various factors between epigenetics and hepatitis progression, and (3) the prediction of efficacy of small interfering RNA (siRNA) in disease gene silence. In addition, we consider the study on the effect of these on each other, and provide a therapeutic effect improvement in the end as molecular biological basis, and promote understanding of the pathogenesis and treatment of hepatitis by quantitative analysis of the factors, for example the combination therapy to show how to improve the efficiency.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
1: 当初の計画以上に進展している
理由
In this period, among the three targeted problems, we achieved good result and progress on the first one of understanding the molecular resistance mechanisms of hepatitis C virus in NS5A to the combination of interferon (IFN) and ribavirin (RBV). We also made significant advancement on the third problem of siRNA by developping a method the learn the design rules for effective siRNA from the available scored siRNA sequences. We received less result on the second problem on the relationships between epigenetics factors and hepatitis progression that mostly caused by the lack of experimental data for computation.
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| 今後の研究の推進方策 |
For the first problem, we will revise and improve the programs and install it as a system. We will continue to learn and detect more descriminative motifs newly provided by hospitals. For the second problem, we will continue to apply our developed methods to the datasets about epigenetic factors in hepatitis in order to have insights on this research direction. For the third problem, we will focus on improving our methods of enriching the siRNA sequences and linear tensor regression. We also work on generating siRNA with highest knockdown efficacy and the evaluation of medical doctors at Chiba University Hospital.
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