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 - 1 Dec 2007
Event7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE - Boston, MA, United States
Duration: 14 Jan 200717 Jan 2007

Publication series

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

Conference

Conference7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE
CountryUnited States
CityBoston, MA
Period14/1/0717/1/07

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ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Bioengineering

Cite this

Zhao, Q., Mitra, P., Lee, D., & Kang, J. (2007). HICCUP: Hierarchical clustering based value imputation using heterogeneous gene expression microarray datasets. In Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE (pp. 71-78). [4375547] (Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE). https://doi.org/10.1109/BIBE.2007.4375547