IEICE TRANSACTIONS on Information
Extraction of Informative Genes from Multiple Microarray Data Integrated by Rank-Based Approach
Summary :
By converting the expression values of each sample into the corresponding rank values, the rank-based approach enables the direct integration of multiple microarray data produced by different laboratories and/or different techniques. In this study, we verify through statistical and experimental methods that informative genes can be extracted from multiple microarray data integrated by the rank-based approach (briefly, integrated rank-based microarray data). First, after showing that a nonparametric technique can be used effectively as a scoring metric for rank-based microarray data, we prove that the scoring results from integrated rank-based microarray data are statistically significant. Next, through experimental comparisons, we show that the informative genes from integrated rank-based microarray data are statistically more significant than those of single-microarray data. In addition, by comparing the lists of informative genes extracted from experimental data, we show that the rank-based data integration method extracts more significant genes than the z-score-based normalization technique or the rank products technique. Public cancer microarray data were used for our experiments and the marker genes list from the CGAP database was used to compare the extracted genes. The GO database and the GSEA method were also used to analyze the functionalities of the extracted genes.
- Publication
- IEICE TRANSACTIONS on Information Vol.E94-D No.4 pp.841-854
- Publication Date
- 2011/04/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E94.D.841
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Data Mining
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Dongwan HONG, Jeehee YOON, Jongkeun LEE, Sanghyun PARK, Jongil KIM, "Extraction of Informative Genes from Multiple Microarray Data Integrated by Rank-Based Approach" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 4, pp. 841-854, April 2011, doi: 10.1587/transinf.E94.D.841.
Abstract: By converting the expression values of each sample into the corresponding rank values, the rank-based approach enables the direct integration of multiple microarray data produced by different laboratories and/or different techniques. In this study, we verify through statistical and experimental methods that informative genes can be extracted from multiple microarray data integrated by the rank-based approach (briefly, integrated rank-based microarray data). First, after showing that a nonparametric technique can be used effectively as a scoring metric for rank-based microarray data, we prove that the scoring results from integrated rank-based microarray data are statistically significant. Next, through experimental comparisons, we show that the informative genes from integrated rank-based microarray data are statistically more significant than those of single-microarray data. In addition, by comparing the lists of informative genes extracted from experimental data, we show that the rank-based data integration method extracts more significant genes than the z-score-based normalization technique or the rank products technique. Public cancer microarray data were used for our experiments and the marker genes list from the CGAP database was used to compare the extracted genes. The GO database and the GSEA method were also used to analyze the functionalities of the extracted genes.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.841/_p
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@ARTICLE{e94-d_4_841,
author={Dongwan HONG, Jeehee YOON, Jongkeun LEE, Sanghyun PARK, Jongil KIM, },
journal={IEICE TRANSACTIONS on Information},
title={Extraction of Informative Genes from Multiple Microarray Data Integrated by Rank-Based Approach},
year={2011},
volume={E94-D},
number={4},
pages={841-854},
abstract={By converting the expression values of each sample into the corresponding rank values, the rank-based approach enables the direct integration of multiple microarray data produced by different laboratories and/or different techniques. In this study, we verify through statistical and experimental methods that informative genes can be extracted from multiple microarray data integrated by the rank-based approach (briefly, integrated rank-based microarray data). First, after showing that a nonparametric technique can be used effectively as a scoring metric for rank-based microarray data, we prove that the scoring results from integrated rank-based microarray data are statistically significant. Next, through experimental comparisons, we show that the informative genes from integrated rank-based microarray data are statistically more significant than those of single-microarray data. In addition, by comparing the lists of informative genes extracted from experimental data, we show that the rank-based data integration method extracts more significant genes than the z-score-based normalization technique or the rank products technique. Public cancer microarray data were used for our experiments and the marker genes list from the CGAP database was used to compare the extracted genes. The GO database and the GSEA method were also used to analyze the functionalities of the extracted genes.},
keywords={},
doi={10.1587/transinf.E94.D.841},
ISSN={1745-1361},
month={April},}
Copy
TY - JOUR
TI - Extraction of Informative Genes from Multiple Microarray Data Integrated by Rank-Based Approach
T2 - IEICE TRANSACTIONS on Information
SP - 841
EP - 854
AU - Dongwan HONG
AU - Jeehee YOON
AU - Jongkeun LEE
AU - Sanghyun PARK
AU - Jongil KIM
PY - 2011
DO - 10.1587/transinf.E94.D.841
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2011
AB - By converting the expression values of each sample into the corresponding rank values, the rank-based approach enables the direct integration of multiple microarray data produced by different laboratories and/or different techniques. In this study, we verify through statistical and experimental methods that informative genes can be extracted from multiple microarray data integrated by the rank-based approach (briefly, integrated rank-based microarray data). First, after showing that a nonparametric technique can be used effectively as a scoring metric for rank-based microarray data, we prove that the scoring results from integrated rank-based microarray data are statistically significant. Next, through experimental comparisons, we show that the informative genes from integrated rank-based microarray data are statistically more significant than those of single-microarray data. In addition, by comparing the lists of informative genes extracted from experimental data, we show that the rank-based data integration method extracts more significant genes than the z-score-based normalization technique or the rank products technique. Public cancer microarray data were used for our experiments and the marker genes list from the CGAP database was used to compare the extracted genes. The GO database and the GSEA method were also used to analyze the functionalities of the extracted genes.
ER -
English
