Effective Enrichment of Gene Expression Data Sets

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.

Published in:
Proceedings of the 11th International Conference on Machine Learning and Applications, 1, 76-81
Presented at:
International Conference on Machine Learning and Applications, Boca Raton, Florida, USA, December, 2012

 Record created 2015-08-21, last modified 2018-03-17

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