Missing Value Imputation Using Correlation Coefficient

被引：1
|
作者：
Manna, Sweta [1 ]
Pati, Soumen Kumar [2 ]
机构：
[1] Maulana Abul Kalam Azad Univ Technol, Dept Comp Sci & Engn, Nadia, W Bengal, India
[2] Maulana Abul Kalam Azad Univ Technol, Dept Bioinformat, Nadia, W Bengal, India
来源：
COMPUTATIONAL INTELLIGENCE IN PATTERN RECOGNITION, CIPR 2020 | 2020年 / 1120卷
关键词：
Microarray data; Missing value imputation; Similarity measurement; Correlation coefficient;
D O I：
10.1007/978-981-15-2449-3_47
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Missing values of microarray dataset are imputed with the help of gene expression sample values. The process by which missing values are calculated is the mean of gene expression sample values and then discretized the sample values. Those discretized values are used to find the similarities between gene expressions with missing value-related genes and genes with no missing values. The gene from without missing values which is most similar of each missing value-related gene is selected, and Pearson's correlation coefficient of the identified gene with all no missing valuerelated genes is calculated. Now, the genes which have higher correlation coefficient with respect to a threshold value are identified. At last, the missing position of the gene is imputed with the mean expression values of the no missing value-related genes which are selected based on correlation coefficient values.
引用
收藏
页码：551 / 558
页数：8
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