一种基于KNN-SVR的基因表达缺失值的估计方法

Missing Value Estimation for Microarray Expression Data Based on KNN-SVR

为了消除不相似基因对基因表达谱中缺失值估计的影响,提出了一种基于KNN-SVR的缺失值估计方法。该方法先通过最近邻法选出与目标基因表达最相似的一组完全基因,再用这些基因通过支持向量回归对缺失值进行估计。还提出了用标准化偏差的方差来度量算法的稳定性和估计值的可信度。该方法通过对基因的过滤提高了缺失值估计的有效性。实验结果表明,KNN-SVR法具有较高的估计精度和稳定性。

In order to exclude the effect of dissimilar genes, a new missing value estimation method based on KNN-SVR is proposed. This method selects a group of complete genes most similar to target genes by K-nearest neighbor （KNN） and uses them to estimate missing values by Support Vector Regression （SVR）. This paper also suggests using the variance of Normalized Root Mean Squared Error （NRMSE） to measure the stability of estimation methods and the reliability of estimated values.This method improves the validity of missing value estimation by filtering genes. The experiment results show that KNN-SVR method has better accuracy and stability.