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Towards Optimal Microarray Universal Reference Sample Designs: An In-Silico Optimization Approach

Abstract : Assessment of the reliability of microarray experiments as well as their cross-laboratory/platform reproducibility rise as the major need. A critical challenge concerns the design of optimal Universal Reference RNA (URR) samples in order to maximize detectable spots in two-color/channel microarray experiments, decrease the variability of microarray data, and finally ease the comparison between heterogeneous microarray datasets. Towards this target we devised and present an in-silico (binary) optimization process the solutions of which present optimal URR sample designs. Setting a cut-off threshold value over which a gene is considered as detectably expressed enables the process. Experimental results are quite encouraging and the related discussion highlights the suitability and flexibility of the approach.
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George Potamias, Sofia Kaforou, Dimitris Kafetzopoulos. Towards Optimal Microarray Universal Reference Sample Designs: An In-Silico Optimization Approach. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. pp.443-452, ⟨10.1007/978-3-642-23957-1_49⟩. ⟨hal-01571362⟩

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