The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Clustering Gene Expression Patterns
Abstract
Recent advances in biotechnology allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. Analysis of data produced by such experiments offers potential insight into gene function and regulatory mechanisms. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. The corresponding algorithmic problem is to cluster multicondition gene expression patterns. In this paper we describe a novel clustering algorithm that was developed for analysis of gene expression data. We define an appropriate stochastic error model on the input, and prove that under the conditions of the model, the algorithm recovers the cluster structure with high probability. The running time of the algorithm on an n-gene dataset is O[n2[log(n)]c]. We also present a practical heuristic based on the same algorithmic ideas. The heuristic was implemented and its performance is demonstrated on simulated data and on real gene expression data, with very promising results.
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Cites
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
Cited by
TM4: a free, open-source system for microarray data management and analysis
Saeed, A. I., Sharov, V., White, J., Li, J., Liang, W., Bhagabati, N., Braisted, J., Klapa, M., Currier, T., Thiagarajan, M., et al. (2003) · BioTechniques · doi:10.2144/03342mt01 · Cited by 4,837
A Comprehensive Survey of Clustering Algorithms
Xu, D., & Tian, Y. (2015) · Annals of Data Science · doi:10.1007/s40745-015-0040-1 · Cited by 1,939
A survey on filter techniques for feature selection in gene expression microarray analysis
Lazar, C., Taminau, J., Meganck, S., Steenhoff, D., Coletta, A., Molter, C., de Schaetzen, V., Duque, R., Bersini, H., & Nowe, A. (2012) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2012.33 · Cited by 619
Enhanced biclustering on expression data
Yang, J., Wang, H., Wang, W., & Yu, P. (2003) · Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. · doi:10.1109/bibe.2003.1188969 · Cited by 299
Review on statistical methods for gene network reconstruction using expression data
Wang, Y. X. R., & Huang, H. (2014) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2014.03.040 · Cited by 182
Metadata
- Authors
- Ben-Dor, A., Shamir, R., & Yakhini, Z.
- Venue
- Journal of Computational Biology
- Publisher
- SAGE Publications
- Published
- 1999-10
- Volume
- 6
- Issue
- 3-4
- Pages
- 281-297
- ISSN
- 1066-5277 (ISSN-L)
- 1557-8666
- OpenAlex
- W2611831635
- Cited by
- 1,203
- References
- 1
- FWCI
- 13.59
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ben-Dor, A., Shamir, R., & Yakhini, Z. (1999). Clustering Gene Expression Patterns. Journal of Computational Biology. https://doi.org/10.1089/106652799318274
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
