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Data integration for microarrays: enhanced inference for gene regulatory networks
Sîrbu, Alina; Crane, Martin; Ruskin, Heather J.
Microarray technologies have been the basis of numerous important findings regarding gene expression in the 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 data sets are integrated, indicating that microarray data will remain a valuable and viable resource for some time to come.
Keyword(s): Bioinformatics; Machine learning; Mathematical models; Statistics; Statistical physics; Data integration; Microarrays; Gene regulatory networks; Transcriptional regulation; Reverse engineering
Publication Date:
2015
Type: Other
Peer-Reviewed: Unknown
Language(s): English
Institution: Dublin City University
Citation(s): Sîrbu, Alina, Crane, Martin ORCID: 0000-0001-7598-3126 <https://orcid.org/0000-0001-7598-3126> and Ruskin, Heather J. ORCID: 0000-0001-7101-2242 <https://orcid.org/0000-0001-7101-2242> (2015) Data integration for microarrays: enhanced inference for gene regulatory networks. Microarrays, 4 (2). pp. 255-269. ISSN 2076-3905
Publisher(s): MDPI - Open Access Publishing
File Format(s): application/pdf
Related Link(s): http://doras.dcu.ie/20584/1/microarrays-Ruskin.pdf,
http://www.mdpi.com/2076-3905/4/2/255
First Indexed: 2015-05-27 05:05:11 Last Updated: 2019-02-09 06:22:50