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  • Microarray Missing Data Imputation based on a Set Theoretic Framework and Biological Knowledge

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    Author(s)
    Gan, XC
    Liew, AWC
    Yan, H
    Griffith University Author(s)
    Liew, Alan Wee-Chung
    Year published
    2006
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    Abstract
    Gene expressions measured using microarrays usually suffer from the missing value problem. However, in many data analysis methods, a complete data matrix is required. Although existing missing value imputation algorithms have shown good performance to deal with missing values, they also have their limitations. For example, some algorithms have good performance only when strong local correlation exists in data while some provide the best estimate when data is dominated by global structure. In addition, these algorithms do not take into account any biological constraint in their imputation. In this paper, we propose a set ...
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    Gene expressions measured using microarrays usually suffer from the missing value problem. However, in many data analysis methods, a complete data matrix is required. Although existing missing value imputation algorithms have shown good performance to deal with missing values, they also have their limitations. For example, some algorithms have good performance only when strong local correlation exists in data while some provide the best estimate when data is dominated by global structure. In addition, these algorithms do not take into account any biological constraint in their imputation. In this paper, we propose a set theoretic framework based on projection onto convex sets (POCS) for missing data imputation. POCS allows us to incorporate different types of a priori knowledge about missing values into the estimation process. The main idea of POCS is to formulate every piece of prior knowledge into a corresponding convex set and then use a convergence-guaranteed iterative procedure to obtain a solution in the intersection of all these sets. In this work, we design several convex sets, taking into consideration the biological characteristic of the data: the first set mainly exploit the local correlation structure among genes in microarray data, while the second set captures the global correlation structure among arrays. The third set (actually a series of sets) exploits the biological phenomenon of synchronization loss in microarray experiments. In cyclic systems, synchronization loss is a common phenomenon and we construct a series of sets based on this phenomenon for our POCS imputation algorithm. Experiments show that our algorithm can achieve a significant reduction of error compared to the KNNimpute, SVDimpute and LSimpute methods.
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    Journal Title
    Nucleic Acids Research
    Volume
    34
    Issue
    5
    Publisher URI
    http://nar.oxfordjournals.org/
    DOI
    https://doi.org/10.1093/nar/gkl047
    Copyright Statement
    © 2006 Gan et al. This article has been published under an open access model.
    Subject
    Environmental Sciences
    Biological Sciences
    Information and Computing Sciences
    Publication URI
    http://hdl.handle.net/10072/15357
    Collection
    • Journal articles

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