CAJ | 학술논문

为了辅助siRNA的设计,从已发表文献中共收集到573个siRNA的实验数据,使用基于统计学习理论的支持向量机(SVM)方法,提取了siRNA序列的碱基对关联性(BBC)特征,然后使用十倍交叉验证方法,对siRNA沉默目标基因的效率进行了预测.结果表明,基于支持向量机,选用多项式核作为核函数的算法具有最高的AUC值(0.73,ROC曲线图)和最高的r值(0.43,Pearson相关系数分析),优于以前基于打分的算法.结果说明,在以后的siRNA的设计中应该更多关注碱基之间的关联信息.
위료보조siRNA적설계,종이발표문헌중공수집도573개siRNA적실험수거,사용기우통계학습이론적지지향량궤(SVM)방법,제취료siRNA서렬적감기대관련성(BBC)특정,연후사용십배교차험증방법,대siRNA침묵목표기인적효솔진행료예측.결과표명,기우지지향량궤,선용다항식핵작위핵함수적산법구유최고적AUC치(0.73,ROC곡선도)화최고적r치(0.43,Pearson상관계수분석),우우이전기우타분적산법.결과설명,재이후적siRNA적설계중응해경다관주감기지간적관련신식.
In order to assist the design of short interfering ribonucleic acids (siRNA), 573 non-redundant siRNAs were collected from published literatures and the relationship between siRNAs sequences and RNA interference (RNAi) effect is analyzed by a support vector machine (SVM) based algorithm relied on a basebase correlation (BBC) feature. The results show that the proposed algorithm has the highest area under curve (AUC) value (0. 73) of the receive operating characteristic (ROC) curve and the greatest r value (0. 43) of the Pearson's correlation coefficient. This indicates that the proposed algorithm is better than the published algorithms on the collected datasets and that more attention should be paid to the base-base correlation information in future siRNA design.