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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2007.6
- 수록면
- 397 - 402 (6page)
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In this paper, we present a method for clustering incomplete Microarray data using alternating optimization in which a prior imputation method is not required. To reduce the influence of imputation in preprocessing, we take an alternative optimization approach to find better estimates during iterative clustering process. This method improves the estimates of missing values by exploiting the cluster information such as cluster centroids and all available non-missing values in each iteration. The clustering results of the proposed method are more significantly relevant to the biological gene annotations than those of other methods, indicating its effectiveness and potential for clustering incomplete gene expression data.
목차
Abstract
1. Introduction
2. The Proposed Method
3. Experimental results
4. Conclusion
Acknowledgement
References
저자소개
참고문헌 (21)
참고문헌 신청
1. [학술지(정기간행물)] - M. Eisen / 1998 / Cluster analysis and display of genome-wide expression pat-terns14868 95
2. [학술지(정기간행물)] - P.Tamayo / 1999 / Interpretingpatters of gene expression with self-organizing maps- methods and application to hematopoietic differen-tiation2912 96 : 2907 ~
3. [학술지(정기간행물)] - S.Tavazoie / 1999 / Sys-tematic determination of genetic network architec-ture 22 : 281 ~ 285
4. [학술지(정기간행물)] - Y.Xu / / Clustering gene expressiondata using a graph-theoretic approach - an applica-
5. [학술지(정기간행물)] - / 2002 / S231- Fuzzy c-means method forclustering microarray data
2. [학술지(정기간행물)] - P.Tamayo / 1999 / Interpretingpatters of gene expression with self-organizing maps- methods and application to hematopoietic differen-tiation2912 96 : 2907 ~
3. [학술지(정기간행물)] - S.Tavazoie / 1999 / Sys-tematic determination of genetic network architec-ture 22 : 281 ~ 285
4. [학술지(정기간행물)] - Y.Xu / / Clustering gene expressiondata using a graph-theoretic approach - an applica-
5. [학술지(정기간행물)] - / 2002 / S231- Fuzzy c-means method forclustering microarray data
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