HICCUP: Hierarchical clustering based value imputation using heterogeneous gene expression microarray datasets

Qiankun Zhao, Prasenjit Mitra, Dongwon Lee, Jaewoo Kang

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    A novel microarray value imputation method, HICCUP1, is presented. HICCUP improves upon existing value imputation methods in the several ways. (1) By judiciously integrating heterogeneous microarray datasets using hierarchical clustering, HICCUP overcomes the limitation of using only single dataset with limited number of samples; (2) Unlike local or global value imputation methods, by mining association rules, HICCUP selects appropriate subsets of the most relevant samples for better value imputation; and (3) by exploiting relationship among the sample space (e.g., cancer vs. non-cancer samples), HICCUP improves the accuracy of value imputation. Experiments with a real prostate cancer microarray dataset verify that HICCUP outperforms existing approaches.

    Original languageEnglish
    Title of host publicationProceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE
    Pages71-78
    Number of pages8
    DOIs
    Publication statusPublished - 2007
    Event7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE - Boston, MA, United States
    Duration: 2007 Jan 142007 Jan 17

    Publication series

    NameProceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

    Other

    Other7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE
    Country/TerritoryUnited States
    CityBoston, MA
    Period07/1/1407/1/17

    ASJC Scopus subject areas

    • Biotechnology
    • Genetics
    • Bioengineering

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