计算机科学 ›› 2022, Vol. 49 ›› Issue (2): 265-271.doi: 10.11896/jsjkx.201100132
任首朋1, 李劲1, 王静茹1, 岳昆2
REN Shou-peng1, LI Jin1, WANG Jing-ru1, YUE Kun2
摘要： 长链非编码RNA(long non-coding RNA,lncRNA)在各种人类复杂疾病中起着重要作用。采用计算方法推断lncRNA-疾病间的潜在关联关系不仅有助于理解疾病的致病机理,还有助于疾病诊断、预防和治疗。文中提出了一种基于集成回归决策树的lncRNA-疾病关联预测方法。首先,利用已知的lncRNA-疾病关联信息分别构建lncRNA、疾病相似矩阵、lncRNA-疾病关联矩阵;其次,基于lncRNA、疾病相似矩阵、lncRNA-疾病关联矩阵,从不同视角进一步构建lncRNA、疾病特征向量;然后,使用主成分分析方法对lncRNA、疾病特征进行特征提取;最后,使用回归决策树作为预测模型,并进一步采用集成学习的平均策略将多个决策树集成,从而获得最终的预测模型。留一交叉验证实验表明,该方法的预测结果优于现有方法,在3个真实的lncRNA-疾病数据集上AUC值分别达到了0.905 5,0.896 9和0.912 9,与现有方法相比,分别提升了6.46%,5.4%和6.02%。此外,对乳腺癌、肺癌、胃癌3种疾病进行了案例分析,进一步验证了所提方法的准确性和有效性。
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