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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Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.443-452, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_49〉
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George Potamias, Sofia Kaforou, Dimitris Kafetzopoulos. Towards Optimal Microarray Universal Reference Sample Designs: An In-Silico Optimization Approach. Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.443-452, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_49〉. 〈hal-01571362〉

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