Results There were no significant difference among the results of the three imputation methods and the original values.
结果不同假设缺失比例下，三种填充方法填充后的结果与原始值相比差异均无统计学意义。
Experimental results confirmed that SLLSk impute method is valid and it exhibited better estimation ability than other imputation methods used currently.
实验结果证实了该算法的有效性，其估计性能优于其它一些常用的填补方法。
Experiments prove that the method is valid and its performance is higher than the other imputation methods based on k-nearest neighbors for gene expression data.
实验结果证明了该算法具有有效性，其性能优于其他基于最近邻居法的缺失值处理算法。
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