Article: A framework for neighbourhood configuration to improve the KNN based imputation algorithms on microarray gene expression data Journal: International Journal of Bioinformatics Research and Applications (IJBRA) 2022 Vol.18 No.3 pp.141 - 190 Abstract: Due to technical problems in DNA microarray experiments, a large number of entries are found missing in microarray datasets. As a consequence, the effectiveness of the analysis algorithms deteriorates. Among different imputation techniques, the weighted average based methods always generate consistent results, are algorithmically simple and very popular but they also suffer from some drawbacks. These deficiencies have been pointed out in this work, and a new framework has been suggested to overcome those. The proposed framework is embedded in the K-nearest neighbour imputation method (KNNimpute), as well as its different versions. It is based on a hybrid distance and gene transformation procedure which utilises simultaneously the advantages of Euclidean distance, mean squared residue score, and Pearson correlation coefficient to select the best possible neighbours, using pattern-based similarity. The framework is tested on well-known microarray datasets. From the experimental results, the superiority of the proposed work has been found. Inderscience Publishers - linking academia, business and industry through research

Title: A framework for neighbourhood configuration to improve the KNN based imputation algorithms on microarray gene expression data

Authors: Shilpi Bose; Chandra Das; Kuntal Ghosh; Matangini Chattopadhyay; Samiran Chattopadhyay

Addresses: Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata, West Bengal, 700152, India ' Department of Computer Science and Engineering, Netaji Subhash Engineering College, Kolkata, West Bengal, 700152, India ' Machine Intelligence Unit, Indian Statistical Institute, Kolkata, West Bengal, 700108, India ' School of Education Technology, Jadavpur University, Kolkata, West Bengal, 700032, India ' Department of Information Technology, Jadavpur University, Kolkata, West Bengal, 700098, India

Abstract: Due to technical problems in DNA microarray experiments, a large number of entries are found missing in microarray datasets. As a consequence, the effectiveness of the analysis algorithms deteriorates. Among different imputation techniques, the weighted average based methods always generate consistent results, are algorithmically simple and very popular but they also suffer from some drawbacks. These deficiencies have been pointed out in this work, and a new framework has been suggested to overcome those. The proposed framework is embedded in the K-nearest neighbour imputation method (KNNimpute), as well as its different versions. It is based on a hybrid distance and gene transformation procedure which utilises simultaneously the advantages of Euclidean distance, mean squared residue score, and Pearson correlation coefficient to select the best possible neighbours, using pattern-based similarity. The framework is tested on well-known microarray datasets. From the experimental results, the superiority of the proposed work has been found.

Keywords: missing value prediction; microarray technology; gene expression data; K-nearest neighbours; Pearson correlation coefficient; mean square residue; euclidean distance.

DOI: 10.1504/IJBRA.2022.124989

International Journal of Bioinformatics Research and Applications, 2022 Vol.18 No.3, pp.141 - 190

Received: 16 Nov 2019
Accepted: 18 Sep 2020
Published online: 22 Aug 2022 *

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Title: A framework for neighbourhood configuration to improve the KNN based imputation algorithms on microarray gene expression data

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