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

Qiankun Zhao; Prasenjit Mitra; Dongwon Lee; Jaewoo Kang

doi:10.1109/BIBE.2007.4375547

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 proceeding › Conference 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 language	English (US)
Title of host publication	Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE
Pages	71-78
Number of pages	8
DOIs	https://doi.org/10.1109/BIBE.2007.4375547
State	Published - 2007
Event	7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE - Boston, MA, United States Duration: Jan 14 2007 → Jan 17 2007

Publication series

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

Other

Other	7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE
Country/Territory	United States
City	Boston, MA
Period	1/14/07 → 1/17/07

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Biotechnology
Genetics
Bioengineering

Access to Document

10.1109/BIBE.2007.4375547

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). Article 4375547 (Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE). https://doi.org/10.1109/BIBE.2007.4375547

Zhao, Qiankun ; Mitra, Prasenjit ; Lee, Dongwon et al. / HICCUP : Hierarchical clustering based value imputation using heterogeneous gene expression microarray datasets. Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE. 2007. pp. 71-78 (Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE).

@inproceedings{f2080028f6b348bf8dc38b81e4f05600,

title = "HICCUP: Hierarchical clustering based value imputation using heterogeneous gene expression microarray datasets",

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.",

author = "Qiankun Zhao and Prasenjit Mitra and Dongwon Lee and Jaewoo Kang",

year = "2007",

doi = "10.1109/BIBE.2007.4375547",

language = "English (US)",

isbn = "1424415098",

series = "Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE",

pages = "71--78",

booktitle = "Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE",

note = "7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE ; Conference date: 14-01-2007 Through 17-01-2007",

}

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., 4375547, Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE, pp. 71-78, 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE, Boston, MA, United States, 1/14/07. https://doi.org/10.1109/BIBE.2007.4375547

HICCUP: Hierarchical clustering based value imputation using heterogeneous gene expression microarray datasets. / Zhao, Qiankun; Mitra, Prasenjit; Lee, Dongwon et al.
Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE. 2007. p. 71-78 4375547 (Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - HICCUP

T2 - 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

AU - Zhao, Qiankun

AU - Mitra, Prasenjit

AU - Lee, Dongwon

AU - Kang, Jaewoo

PY - 2007

Y1 - 2007

N2 - 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.

AB - 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.

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U2 - 10.1109/BIBE.2007.4375547

DO - 10.1109/BIBE.2007.4375547

M3 - Conference contribution

AN - SCOPUS:47649132444

SN - 1424415098

SN - 9781424415090

T3 - Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

SP - 71

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BT - Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

Y2 - 14 January 2007 through 17 January 2007

ER -

Zhao Q, Mitra P, Lee D, Kang J. 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. 2007. p. 71-78. 4375547. (Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE). doi: 10.1109/BIBE.2007.4375547

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

Abstract

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All Science Journal Classification (ASJC) codes

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