More Web Proxy on the site http://driver.im/

article

Graphical modeling based gene interaction analysis for microarray data

Authors:

Liying ZhangAuthors Info & Claims

ACM SIGKDD Explorations Newsletter, Volume 5, Issue 2

Pages 91 - 100

https://doi.org/10.1145/980972.980984

Published: 01 December 2003 Publication History

Abstract

DNA Microarray provides a powerful basis for analysis of gene expression. Data mining methods such as clustering have been widely applied to microarray data to link genes that show similar expression patterns. However, this approach usually fails to unveil gene-gene interactions in the same cluster. In this paper, we propose to use graphical modeling based interaction analysis for this purpose. We apply graphical gaussian model to discover pairwise gene interactions and use loglinear model to discover multi-gene interactions. We have constructed a prototype system that permits rapid interactive exploration of gene relationships; results can be validated by experts or known information, or suggest new experiments. We have tested our methodology using the yeast microarray data. Our results reveal some previously unknown interactions that have solid biological explanations.

References

[1]

R. Agrawal, T. Imilienski, and A. Swami. Mining association rules between sets of items in large database In Proceedings of the ACM SIGMOD International Conference on Management of Database, pages 207--216, 1993.

Digital Library

[2]

E. Andersen. The statistical anlysis of categorical data. Springer Verlag, Berlin, Heidelberg, 1994.

[3]

M. Ashburner, C. Ball, J. Blake, and et al. Gene ontology: tool for the unification of biology the gene ontology consortium. Nat Genet, 25:25--29, 2000.

[4]

G. Bader and C. Hogue. Bind: a data specification for storing and describing biomolecular interactions, molecular complexes and pathways. Bioinformatics, 16:465--477, 2000.

[5]

A. Ben-Dor, R. Shamir, and Z. Yakhini. Clustering gene expression patterns. Journal of Computational Biology, 6(3/4):281--297, 1999.

[6]

C. Borgelt. Association rule induction. http://fuzzy.cs.unimagdeburg.de/borgelt/software.html.

[7]

Y. Cheng and G. Church. Biclustering of expression data. In Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology, pages 93--103. San Diego, CA, August 2000.

Digital Library

[8]

C. Creighton and S. Hanash. Mining gene expression databases for association rules. Bioinformatics, 19-1:79--86, 2003.

[9]

K. Dahlquist, N. Salomonist, K. Vranizan, S. Lawlor, and B. Conklin. Genmapp, a new tool for viewing and analyzing microarray data on biological pathways. Nat Genet, 31:19--20, 2002.

[10]

W. DuMouchel and D. Pregibon. Empirical bayes screening for multi-item association. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data. San Francisco, CA, August 2001.

Digital Library

[11]

M. Eisen. Cluster analysis and visualization. http://rana.lbl.gov.

[12]

J. Ellson and S. North. Graph visualization project (graphviz). http://www.graphviz.org.

[13]

N. Friedman, M. Linial, I. Nachman, and D. Peer. Using bayesian networks to analyze expression data. In Proceedings of the fourth Annual International Conference on Computational Molecular Biology, 2000.

Digital Library

[14]

L. Goodman. The analysis of multidimensional contigency tables: Stepwise procedures and direct estimation methods for building models for multiple classifications. Technometrics, 13:33--61, 1971.

[15]

E. Hartuv and R. Shamir. A clustering algorithm based on graph connectivity. Information Processing Letters, 76(4--6):175--181, 2000.

Digital Library

[16]

T. Hughes, M. Marton, A. R. Jones, C. Roberts, R. Stoughton, C. Armour, H. Bennett, E. Coffey, H. Dai, Y. He, M. J. Kidd, and A. M. King. Functional discovery via a compendium of expression profiles. Cell, 102:109--126, 2000.

[17]

D. Kinney and C. Lusty. Arginine restriction induced by delta-n-(phosphonacetyl)-l-ornithine signals increased expression of his3, trp5, cpal, and cpa2 in saccharomyces cerevisiae. Mol. Cell Boil, 9:4882--4888, 1989.

[18]

H. Kishino and P. J. Waddell. Correspondence analysis of genes and tissue types and finding genetic links from microarray data. Genome Informatics, 11:83--95, 2000.

[19]

P. A. Padilla, E. K. Fuge, M. E. Crawford, A. Errett, and M. Werner-Washburne. The highly conserved, coregulated sno and snz gene families in saccharomyces cerevisiae respond to nutrient limitation. Journal of Bacteriol, 180:5718--5726, 1998.

[20]

S. Sarawagi, R. Agrawal, and N. Meggido. Discoverydriven exploration of olap data cubes. In Proceedings of the International Conference on Extending Data Base Technology, pages 168--182. Valencia, Spain, 1998.

Digital Library

[21]

E. Segal, M. Shapira, A. Regev, D. Peer, D. Botstein, D. Koller, and N. Friedman. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nature Genetics, 34:166--176, 2003.

[22]

R. Shamir and R. Shamir. Click: A clustering algorithm for gene expression analysis. In Proceedings of the Eighth International Conference on Intelligent System for Molecular Biology (ISMB00), 2000.

[23]

C. Silverstein, S. Brin, and R. Motwani. Beyond market baskets: generalizing association rules to dependence rules. Data Mining and Knowledge Discovery, 2:39--68, 1998.

Digital Library

[24]

B. Spitzak and et. al. The fast light toolkit(fltk). http://www.fltk.org.

[25]

R. Tarjan. Decomposition by clique separators. Discrete Mathematics, 55:221--232, 1985.

[26]

P. Uetz, L. Giot, G. Cagney, and et al. A comprehensive analysis of protein-protein interactions in saccharomyces cerevisiase. Nature, 403:623--627, 2000.

[27]

J. Venter, M. Adams, E. Myers, and et al. The sequence of the human genome. Science, 291:1304--1351, 2001.

[28]

J. Whittaker. Graphical Models in Applied Mathematical Multivariate Statistics. Wiley, 1990.

[29]

X. Wu, D. Barbará, and Y. Ye. Screening and interpreting multi-item associations based on log-linear modeling. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 276--285. Washington, DC, August 2003.

Digital Library

[30]

X. Wu, D. Barbará, L. Zhang, and Y. Ye. Gene interaction analysis using k-way interaction loglinear model: A case study on yeast data. In ICML03 Workshop on Machine Learning in Bioinformatics, pages 38--45. Washington, DC, August 2003.

[31]

X. Wu, Y. Ye, K. Subramanian, and L. Zhang. Interactive gene interaction analysis using graphical gaussian models. In The 3rd ACM SIGKDD Workshop on Data Mining in Bioinformatics, pages 63--69. Washington, DC, August 2003.

[32]

I. Xenarios, L. Salwinski, X. Duan, P. Higney, S. M. Kim, and D. Eisenberg. Dip, the database of interacting proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Res, 30:303--305, 2002.

[33]

Y. Xu, V. Olman, and D. Xu. Clustering gene expression data using a graph-theoretic approach: an application of minimum spanning trees. Bioinformatics, 18(4):536--545, 2002.

Cited By

Wu XYe Y(2006)Exploring gene causal interactions using an enhanced constraint-based methodPattern Recognition10.1016/j.patcog.2006.05.00339:12(2439-2449)Online publication date: 1-Dec-2006
https://dl.acm.org/doi/10.1016/j.patcog.2006.05.003
Hanczar B(2005)Combining feature selection and feature construction to improve concept learning for high dimensional dataProceedings of the 6th international conference on Abstraction, Reformulation and Approximation10.1007/11527862_19(261-273)Online publication date: 26-Jul-2005
https://dl.acm.org/doi/10.1007/11527862_19
Ye YWu X(2005)Efficient causal interaction learning with applications in microarrayProceedings of the 15th international conference on Foundations of Intelligent Systems10.1007/11425274_64(622-630)Online publication date: 25-May-2005
https://dl.acm.org/doi/10.1007/11425274_64

Graphical modeling based gene interaction analysis for microarray data

Recommendations

Gene expression and protein---protein interaction data for identification of colon cancer related genes using f-information measures

One of the most important and challenging problems in functional genomics is how to select the disease genes. In this regard, the paper presents a new computational method to identify disease genes. It judiciously integrates the information of gene ...
NFB pathway analysis

Display Omitted An analysis based on cycles among genes of gene co-expression networks is proposed.Cycles are associated with feedback mechanisms, very common in biological networks.NFB pathway analysis in tumor specimens of GBM compared to normal brain ...
Identifying Biologically Significant Pathways by Gene Set Enrichment Analysis Using Fisher's Criterion
FGCN '08: Proceedings of the 2008 Second International Conference on Future Generation Communication and Networking - Volume 03

Gene set enrichment analysis (GSEA) is a computational method to identify statistically significant gene-sets showing differential expression between two groups. In particular, unlike other previous approaches, this enables us to uncover their ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM SIGKDD Explorations Newsletter

ACM SIGKDD Explorations Newsletter Volume 5, Issue 2

December 2003

202 pages

ISSN:1931-0145

EISSN:1931-0153

DOI:10.1145/980972

Issue’s Table of Contents

Copyright © 2003 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 December 2003

Published in SIGKDD Volume 5, Issue 2

Check for updates

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
864
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 11 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Wu XYe Y(2006)Exploring gene causal interactions using an enhanced constraint-based methodPattern Recognition10.1016/j.patcog.2006.05.00339:12(2439-2449)Online publication date: 1-Dec-2006
https://dl.acm.org/doi/10.1016/j.patcog.2006.05.003
Hanczar B(2005)Combining feature selection and feature construction to improve concept learning for high dimensional dataProceedings of the 6th international conference on Abstraction, Reformulation and Approximation10.1007/11527862_19(261-273)Online publication date: 26-Jul-2005
https://dl.acm.org/doi/10.1007/11527862_19
Ye YWu X(2005)Efficient causal interaction learning with applications in microarrayProceedings of the 15th international conference on Foundations of Intelligent Systems10.1007/11425274_64(622-630)Online publication date: 25-May-2005
https://dl.acm.org/doi/10.1007/11425274_64

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents