title: Missing value imputation for gene expression data: computational techniques to recover missing data from available information.
abstract: Microarray gene expression data generally suffers from missing value problem due to a variety of experimental reasons. Since the missing data points can adversely affect downstream analysis, many algorithms have been proposed to impute missing values. In this survey, we provide a comprehensive review of existing missing value imputation algorithms, focusing on their underlying algorithmic techniques and how they utilize local or global information from within the data, or their use of domain knowledge during imputation. In addition, we describe how the imputation results can be validated and the different ways to assess the performance of different imputation algorithms, as well as a discussion on some possible future research directions. It is hoped that this review will give the readers a good understanding of the current development in this field and inspire them to come up with the next generation of imputation algorithms.
authors.affiliation: School of Information and Communication Technology, Gold Coast Campus, Griffith University, QLD4222, Australia. a.liew@griffith.edu.au
authors.forename: Alan Wee-Chung
authors.initials: AW
authors.lastname: Liew
authors.forename: Ngai-Fong
authors.initials: NF
authors.lastname: Law
authors.forename: Hong
authors.initials: H
authors.lastname: Yan
mesh_terms: D000465:Algorithms; D000818:Animals; D016208:Databases, Factual; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D046228:Microarray Analysis / Q000379:methods