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Extracting Dynamics from Static Cancer Expression Data

Authors:

Ziv Bar-JosephAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 5, Issue 2

Pages 172 - 182

https://doi.org/10.1109/TCBB.2007.70233

Published: 01 April 2008 Publication History

Abstract

Static expression experiments analyze samples from many individuals. These samples are often snapshots of the progression of a certain disease such as cancer. This raises an intriguing question: Can we determine a temporal order for these samples? Such an ordering can lead to better understanding of the dynamics of the disease and to the identification of genes associated with its progression. In this paper we formally prove, for the first time, that under a model for the dynamics of the expression levels of a single gene, it is indeed possible to recover the correct ordering of the static expression datasets by solving an instance of the traveling salesman problem (TSP). In addition, we devise an algorithm that combines a TSP heuristic and probabilistic modeling for inferring the underlying temporal order of the microarray experiments. This algorithm constructs probabilistic continuous curves to represent expression profiles leading to accurate temporal reconstruction for human data. Applying our method to cancer expression data we show that the ordering derived agrees well with survival duration. A classifier that utilizes this ordering improves upon other classifiers suggested for this task. The set of genes displaying consistent behavior for the determined ordering are enriched for genes associated with cancer progression.

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Cited By

Graudenzi ACaravagna GBocicor ICava CAntoniotti MMauri G(2016)Ordering cancer mutational profiles of cross-sectional copy number alterationsInternational Journal of Data Mining and Bioinformatics10.1504/IJDMB.2016.07601715:1(59-83)Online publication date: 1-Apr-2016
https://dl.acm.org/doi/10.1504/IJDMB.2016.076017
Huang TSchneider J(2011)Learning Auto-regressive models from sequence and non-sequence dataProceedings of the 25th International Conference on Neural Information Processing Systems10.5555/2986459.2986632(1548-1556)Online publication date: 12-Dec-2011
https://dl.acm.org/doi/10.5555/2986459.2986632
Antoniotti MCarreras MAntonella FMauri GMerico DZoppis I(2010)An application of kernel methods to gene cluster temporal meta-analysisComputers and Operations Research10.1016/j.cor.2009.03.01137:8(1361-1368)Online publication date: 1-Aug-2010
https://dl.acm.org/doi/10.1016/j.cor.2009.03.011

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cover image IEEE/ACM Transactions on Computational Biology and Bioinformatics

IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 5, Issue 2

April 2008

158 pages

ISSN:1545-5963

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IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 April 2008

Published in TCBB Volume 5, Issue 2

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Cited By

Graudenzi ACaravagna GBocicor ICava CAntoniotti MMauri G(2016)Ordering cancer mutational profiles of cross-sectional copy number alterationsInternational Journal of Data Mining and Bioinformatics10.1504/IJDMB.2016.07601715:1(59-83)Online publication date: 1-Apr-2016
https://dl.acm.org/doi/10.1504/IJDMB.2016.076017
Huang TSchneider J(2011)Learning Auto-regressive models from sequence and non-sequence dataProceedings of the 25th International Conference on Neural Information Processing Systems10.5555/2986459.2986632(1548-1556)Online publication date: 12-Dec-2011
https://dl.acm.org/doi/10.5555/2986459.2986632
Antoniotti MCarreras MAntonella FMauri GMerico DZoppis I(2010)An application of kernel methods to gene cluster temporal meta-analysisComputers and Operations Research10.1016/j.cor.2009.03.01137:8(1361-1368)Online publication date: 1-Aug-2010
https://dl.acm.org/doi/10.1016/j.cor.2009.03.011

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