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From the third issue of 2017, Microarrays has changed its name to High-Throughput.

Open AccessArticle
Microarrays 2015, 4(2), 255-269; doi:10.3390/microarrays4020255

Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks

1
Department of Computer Science and Engineering, University of Bologna, Via Mura Anteo Zamboni 7, Bologna 40126, Italy
2
Center for Scientific Computing and Complex Systems Modelling, School of Computing, Dublin City University, Glasnevin, Dublin 9, Ireland
*
Author to whom correspondence should be addressed.
Academic Editor: Ulrich Certa
Received: 27 February 2015 / Accepted: 30 April 2015 / Published: 14 May 2015
(This article belongs to the Special Issue Computational Modeling and Analysis of Microarray Data: New Horizons)
View Full-Text   |   Download PDF [129 KB, uploaded 14 May 2015]   |  

Abstract

Microarray technologies have been the basis of numerous important findings regarding gene expression in the few last decades. Studies have generated large amounts of data describing various processes, which, due to the existence of public databases, are widely available for further analysis. Given their lower cost and higher maturity compared to newer sequencing technologies, these data continue to be produced, even though data quality has been the subject of some debate. However, given the large volume of data generated, integration can help overcome some issues related, e.g., to noise or reduced time resolution, while providing additional insight on features not directly addressed by sequencing methods. Here, we present an integration test case based on public Drosophila melanogaster datasets (gene expression, binding site affinities, known interactions). Using an evolutionary computation framework, we show how integration can enhance the ability to recover transcriptional gene regulatory networks from these data, as well as indicating which data types are more important for quantitative and qualitative network inference. Our results show a clear improvement in performance when multiple datasets are integrated, indicating that microarray data will remain a valuable and viable resource for some time to come. View Full-Text
Keywords: data integration; microarrays; gene regulatory networks; transcriptional regulation; reverse engineering data integration; microarrays; gene regulatory networks; transcriptional regulation; reverse engineering
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Sîrbu, A.; Crane, M.; Ruskin, H.J. Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks. Microarrays 2015, 4, 255-269.

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