A novel distance-based iterative sequential KNN algorithm for estimation of missing values in microarray gene expression data

摘要

The presence of missing entries in DNA microarray gene expression datasets creates severe problems in downstream analysis because they require complete datasets. Though several missing value prediction methods have been proposed to solve this problem, they have limitations which may affect the performance of various analysis algorithms. In this regard, a novel distance based iterative sequential K-nearest neighbour imputation method (IS/GSTNimpute) has been proposed. The proposed distance is a hybridisation of modified Euclidean distance and Pearson correlation coefficient. The proposed method is a modification of KNN estimation in which the concept of reuse of estimation is considered using both iterative and sequential approach. The performance of the proposed ISAMNimpute method is tested on various time-series and non time-series microarray datasets comparing with several widely used existing imputation techniques. The experimental results confirm that the ISKNNimpute method consistently generates better results compared to other existing methods.