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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/12169

Title: 計算アプローチによる肝炎の病態・治療に関する分子機構の解明
Other Titles: Elucidation of the molecular mechanisms on the pathology and treatment of hepatitis by computational approach
Authors: Ho, Tu Bao
Keywords: 医薬生物
統計解析
データマイニング
Issue Date: 9-Jun-2014
Abstract: 第1課題のHCV NS5AのIFN-RBV治療への耐性機序について、多数のNS5Aデータにおいて著効データが少数である場合に有効な準教師付アンサンブル学習手法を開発し、SVRと非SVRの2種の患者群を特徴づけるモチーフを発見した。第2課題のsiRNA抑制効果用に開発した手法では、既知の設計規則に加え2種の新siRNA設計規則を発見し、また他手法では所与のsiRNA配列についてスコアと著効情報をもつ配列と既知の規則で配列の表現力を補強し、新規のテンソル回帰手法にて予測精度を大幅に改善した。第3課題ではエピジェネティク因子と肝炎進行の相互作用に関するヒストン修飾の因果関係網等の中間結果を得た。 : For the first task on HCV NS5A resistance mechanisms to interferon/ribavirin therapy, our semi-supervised ensemble method discovered the motifs that well characterizing two class of patients with SVR and non-SVR, especially in case of only a small number labelled NS5A sequences but much unlabelled sequences are available. For the second task on the knockdown efficacy of siRNA, one of our method discovered two siRNA design rules in addition to known design rules, and the other significantly improve the predictive ability for given siRNA sequences. This method employs both scored and labelled sequences as well as known design rules to enrich the poor sequence representation of siRNA by transformed matrices. The prediction is done by a novel tensor regression method on those matrices. For the third task on the interplay between epigenetic factors and hepatitis progression, we reached some intermediate results such as inferring the causual relationship network of histone modifications.
Description: 研究種目:基盤研究(B)
研究期間:2011~2013
課題番号:23300105
研究者番号:60301199
研究分野:データマイニング
科研費の分科・細目:情報学・統計科学
Language: eng
URI: http://hdl.handle.net/10119/12169
Appears in Collections:平成25年度 (FY 2013)

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