Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks
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 t...
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doaj-0e3e64402d264aa186abe436dcf36cd52020-11-24T22:01:12ZengMDPI AGMicroarrays2076-39052015-05-014225526910.3390/microarrays4020255microarrays4020255Data Integration for Microarrays: Enhanced Inference for Gene Regulatory NetworksAlina Sîrbu0Martin Crane1Heather J. Ruskin2Department of Computer Science and Engineering, University of Bologna, Via Mura Anteo Zamboni 7, Bologna 40126, ItalyCenter for Scientific Computing and Complex Systems Modelling, School of Computing, Dublin City University, Glasnevin, Dublin 9, IrelandCenter for Scientific Computing and Complex Systems Modelling, School of Computing, Dublin City University, Glasnevin, Dublin 9, IrelandMicroarray 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.http://www.mdpi.com/2076-3905/4/2/255data integrationmicroarraysgene regulatory networkstranscriptional regulationreverse engineering |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Alina Sîrbu Martin Crane Heather J. Ruskin |
spellingShingle |
Alina Sîrbu Martin Crane Heather J. Ruskin Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks Microarrays data integration microarrays gene regulatory networks transcriptional regulation reverse engineering |
author_facet |
Alina Sîrbu Martin Crane Heather J. Ruskin |
author_sort |
Alina Sîrbu |
title |
Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks |
title_short |
Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks |
title_full |
Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks |
title_fullStr |
Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks |
title_full_unstemmed |
Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks |
title_sort |
data integration for microarrays: enhanced inference for gene regulatory networks |
publisher |
MDPI AG |
series |
Microarrays |
issn |
2076-3905 |
publishDate |
2015-05-01 |
description |
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. |
topic |
data integration microarrays gene regulatory networks transcriptional regulation reverse engineering |
url |
http://www.mdpi.com/2076-3905/4/2/255 |
work_keys_str_mv |
AT alinasirbu dataintegrationformicroarraysenhancedinferenceforgeneregulatorynetworks AT martincrane dataintegrationformicroarraysenhancedinferenceforgeneregulatorynetworks AT heatherjruskin dataintegrationformicroarraysenhancedinferenceforgeneregulatorynetworks |
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