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Microarray Bioinformatics pp 255–266Cite as

Missing-Values Imputation Algorithms for Microarray Gene Expression Data

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1986))

Abstract

In gene expression studies, missing values are a common problem with important consequences for the interpretation of the final data (Satija et al., Nat Biotechnol 33(5):495, 2015). Numerous bioinformatics examination tools are used for cancer prediction, including the data set matrix (Bailey et al., Cell 173(2):371–385, 2018); thus, it is necessary to resolve the problem of missing-values imputation. This chapter presents a review of the research on missing-values imputation approaches for gene expression data. By using local and global correlation of the data, we were able to focus mostly on the differences between the algorithms. We classified the algorithms as global, hybrid, local, or knowledge-based techniques. Additionally, this chapter presents suitable assessments of the different approaches. The purpose of this review is to focus on developments in the current techniques for scientists rather than applying different or newly developed algorithms with identical functional goals. The aim was to adapt the algorithms to the characteristics of the data.

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Acknowledgements

We would like to thank Universiti Malaysia Pahang for supporting this work under the RDU Grant, Grant number: RDU1703200 and RDU180344.

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Authors and Affiliations

  1. Faculty of Computer Systems & Software Engineering, Universiti Malaysia Pahang, Kuantan, Pahang, Malaysia

    Kohbalan Moorthy, Aws Naser Jaber, Mohd Arfian Ismail & Ferda Ernawan

  2. Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, Kota Bharu, Kelantan, Malaysia

    Mohd Saberi Mohamad & Safaai Deris

Authors
  1. Kohbalan Moorthy
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  2. Aws Naser Jaber
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  3. Mohd Arfian Ismail
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  4. Ferda Ernawan
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  5. Mohd Saberi Mohamad
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  6. Safaai Deris
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Corresponding author

Correspondence to Kohbalan Moorthy .

Editor information

Editors and Affiliations

  1. CITIC, Universidade da Coruña, A Coruña, Spain

    Verónica Bolón-Canedo

  2. CITIC, Universidade da Coruña, A Coruña, Spain

    Amparo Alonso-Betanzos

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Moorthy, K., Jaber, A.N., Ismail, M.A., Ernawan, F., Mohamad, M.S., Deris, S. (2019). Missing-Values Imputation Algorithms for Microarray Gene Expression Data. In: Bolón-Canedo, V., Alonso-Betanzos, A. (eds) Microarray Bioinformatics. Methods in Molecular Biology, vol 1986. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9442-7_12

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  • DOI: https://doi.org/10.1007/978-1-4939-9442-7_12

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-9441-0

  • Online ISBN: 978-1-4939-9442-7

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