Sehgal, M. Shoaib; Gondal, Iqbal; Dooley, Laurence S. and Coppel, Ross
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|DOI (Digital Object Identifier) Link:||http://doi.org/10.1007/978-3-540-88436-1|
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Microarrays are able to measure the patterns of expression of thousands of genes in a genome to give profiles that facilitate much faster analysis of biological processes for diagnosis, prognosis and tailored drug discovery. Microarrays, however, commonly have missing values which can result in erroneous downstream analysis. To impute these missing values, various algorithms have been proposed including Collateral Missing Value Estimation (CMVE), Bayesian Principal Component Analysis (BPCA), Least Square Impute (LSImpute), Local Least Square Impute (LLSImpute) and K-Nearest Neighbour (KNN). Most of these imputation algorithms exploit either the global or local correlation structure of the data, which normally leads to larger estimation errors. This paper presents an enhanced Heuristic Non Parametric Collateral Missing Value Imputation (HCMVI) algorithm which uses CMVE as its core estimator and Heuristic Non Parametric strategy to compute optimal number of estimator genes to exploit optimally both local and global correlations.
|Item Type:||Book Chapter|
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Interdisciplinary Research Centre:||Centre for Research in Computing (CRC)|
|Depositing User:||Laurence Dooley|
|Date Deposited:||01 Jul 2008|
|Last Modified:||05 Aug 2016 18:20|
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