Effective Enrichment of Gene Expression Data Sets

Date

2012-12-15

Author

Sirin, Utku
Erdogdu, Utku
TAN, MEHMET
Polat, Faruk
Alhajj, Reda

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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The ever-growing need for gene-expression data analysis motivates studies in sample generation due to the lack of enough gene-expression data. It is common that there are thousands of genes but only tens or rarely hundreds of samples available. In this paper, we attempt to formulate the sample generation task as follows: first, building alternative Gene Regulatory Network (GRN) models; second, sampling data from each of them; and then filtering the generated samples using metrics that measure compatibility, diversity and coverage with respect to the original dataset. We constructed two alternative GRN models using Probabilistic Boolean Networks and Ordinary Differential Equations. We developed a multi-objective filtering mechanism based on the three metrics to assess the quality of the newly generated data. We presented a number of experiments to show effectiveness and applicability of the proposed multi-model framework.

Subject Keywords

Gene Expression Data, Sample Generation, Multiple Perspectives, Learning, Gene Regulation Modeling, Probabilistic Boolean Networks, Ordinary Differential Equations

URI

https://hdl.handle.net/11511/40677

DOI

https://doi.org/10.1109/icmla.2012.22

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

U. Sirin, U. Erdogdu, M. TAN, F. Polat, and R. Alhajj, “Effective Enrichment of Gene Expression Data Sets,” 2012, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/40677.