@article {804, title = {Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks}, journal = {Microarrays}, volume = {4}, number = {2}, year = {2015}, pages = {255{\textendash}269}, doi = {10.3390/microarrays4020255}, url = {http://www.mdpi.com/2076-3905/4/2/255}, author = {Alina Sirbu and Martin Crane and Heather J Ruskin} } @inbook {808, title = {EGIA{\textendash}Evolutionary Optimisation of Gene Regulatory Networks, an Integrative Approach}, booktitle = {Complex Networks V}, year = {2014}, pages = {217{\textendash}229}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, doi = {10.1007/978-3-319-05401-8_21}, url = {http://link.springer.com/chapter/10.1007/978-3-319-05401-8_21}, author = {Alina Sirbu and Martin Crane and Heather J Ruskin} } @article {798, title = {Integrating heterogeneous gene expression data for gene regulatory network modelling.}, journal = {Theory Biosci}, volume = {131}, year = {2012}, month = {2012 Jun}, pages = {95-102}, abstract = {

Gene regulatory networks (GRNs) are complex biological systems that have a large impact on protein levels, so that discovering network interactions is a major objective of systems biology. Quantitative GRN models have been inferred, to date, from time series measurements of gene expression, but at small scale, and with limited application to real data. Time series experiments are typically short (number of time points of the order of ten), whereas regulatory networks can be very large (containing hundreds of genes). This creates an under-determination problem, which negatively influences the results of any inferential algorithm. Presented here is an integrative approach to model inference, which has not been previously discussed to the authors{\textquoteright} knowledge. Multiple heterogeneous expression time series are used to infer the same model, and results are shown to be more robust to noise and parameter perturbation. Additionally, a wavelet analysis shows that these models display limited noise over-fitting within the individual datasets.

}, issn = {1611-7530}, doi = {10.1007/s12064-011-0133-0}, author = {Alina Sirbu and Heather J Ruskin and Martin Crane} } @article {796, title = {RNA-Seq vs dual- and single-channel microarray data: sensitivity analysis for differential expression and clustering.}, journal = {PLoS One}, volume = {7}, year = {2012}, month = {2012}, pages = {e50986}, abstract = {

With the fast development of high-throughput sequencing technologies, a new generation of genome-wide gene expression measurements is under way. This is based on mRNA sequencing (RNA-seq), which complements the already mature technology of microarrays, and is expected to overcome some of the latter{\textquoteright}s disadvantages. These RNA-seq data pose new challenges, however, as strengths and weaknesses have yet to be fully identified. Ideally, Next (or Second) Generation Sequencing measures can be integrated for more comprehensive gene expression investigation to facilitate analysis of whole regulatory networks. At present, however, the nature of these data is not very well understood. In this paper we study three alternative gene expression time series datasets for the Drosophila melanogaster embryo development, in order to compare three measurement techniques: RNA-seq, single-channel and dual-channel microarrays. The aim is to study the state of the art for the three technologies, with a view of assessing overlapping features, data compatibility and integration potential, in the context of time series measurements. This involves using established tools for each of the three different technologies, and technical and biological replicates (for RNA-seq and microarrays, respectively), due to the limited availability of biological RNA-seq replicates for time series data. The approach consists of a sensitivity analysis for differential expression and clustering. In general, the RNA-seq dataset displayed highest sensitivity to differential expression. The single-channel data performed similarly for the differentially expressed genes common to gene sets considered. Cluster analysis was used to identify different features of the gene space for the three datasets, with higher similarities found for the RNA-seq and single-channel microarray dataset.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0050986}, author = {Alina Sirbu and Kerr, Gr{\'a}inne and Martin Crane and Heather J Ruskin} } @article {814, title = {Wisdom of crowds for robust gene network inference}, journal = {Nature Methods}, volume = {9}, number = {8}, year = {2012}, pages = {796-804}, doi = {10.1038/nmeth.2016}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84870305264\&partnerID=40\&md5=04a686572bdefff60157bf68c95df7ea}, author = {Daniel Marbach and J.C. Costello and Robert K{\"u}ffner and N.M. Vega and R.J. Prill and D.M. Camacho and K.R. Allison and Manolis Kellis and J.J. Collins and Aderhold, A. and Gustavo Stolovitzky and Bonneau, R. and Chen, Y. and Cordero, F. and Martin Crane and Dondelinger, F. and Drton, M. and Esposito, R. and Foygel, R. and De La Fuente, A. and Gertheiss, J. and Geurts, P. and Greenfield, A. and Grzegorczyk, M. and Haury, A.-C. and Holmes, B. and Hothorn, T. and Husmeier, D. and Huynh-Thu, V.A. and Irrthum, A. and Karlebach, G. and Lebre, S. and De Leo, V. and Madar, A. and Mani, S. and Mordelet, F. and Ostrer, H. and Ouyang, Z. and Pandya, R. and Petri, T. and Pinna, A. and Poultney, C.S. and Rezny, S. and Heather J Ruskin and Saeys, Y. and Shamir, R. and Alina Sirbu and Song, M. and Soranzo, N. and Statnikov, A. and N.M. Vega and Vera-Licona, P. and Vert, J.-P. and Visconti, A. and Haizhou Wang and Wehenkel, L. and Windhager, L. and Zhang, Y. and Zimmer, R.} } @inbook {811, title = {Stages of Gene Regulatory Network Inference: the Evolutionary Algorithm Role}, booktitle = {Evolutionary Algorithms}, year = {2011}, publisher = {InTech}, organization = {InTech}, doi = {DOI: 10.5772/15182}, url = {http://www.intechopen.com/articles/show/title/stages-of-gene-regulatory-network-inference-the-evolutionary-algorithm-role}, author = {Alina Sirbu and Heather J Ruskin and Martin Crane} } @article {800, title = {Comparison of evolutionary algorithms in gene regulatory network model inference.}, journal = {BMC Bioinformatics}, volume = {11}, year = {2010}, month = {2010}, pages = {59}, abstract = {

BACKGROUND: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient.

RESULTS: This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and offer a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared.

CONCLUSIONS: Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identified and a platform for development of appropriate model formalisms is established.

}, issn = {1471-2105}, doi = {10.1186/1471-2105-11-59}, author = {Alina Sirbu and Heather J Ruskin and Martin Crane} } @article {799, title = {Cross-platform microarray data normalisation for regulatory network inference.}, journal = {PLoS One}, volume = {5}, year = {2010}, month = {2010}, pages = {e13822}, abstract = {

BACKGROUND: Inferring Gene Regulatory Networks (GRNs) from time course microarray data suffers from the dimensionality problem created by the short length of available time series compared to the large number of genes in the network. To overcome this, data integration from diverse sources is mandatory. Microarray data from different sources and platforms are publicly available, but integration is not straightforward, due to platform and experimental differences.

METHODS: We analyse here different normalisation approaches for microarray data integration, in the context of reverse engineering of GRN quantitative models. We introduce two preprocessing approaches based on existing normalisation techniques and provide a comprehensive comparison of normalised datasets.

CONCLUSIONS: Results identify a method based on a combination of Loess normalisation and iterative K-means as best for time series normalisation for this problem.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0013822}, author = {Alina Sirbu and Heather J Ruskin and Martin Crane} } @article {812, title = {Regulatory network modelling: Correlation for structure and parameter optimisation}, journal = {Proceedings of The IASTED Technology Conferences (International Conference on Computational Bioscience), Cambridge, Massachusetts}, year = {2010}, pages = {3473{\textendash}3481}, doi = {10.2316/P.2010.728-020}, url = {http://www.actapress.com/Abstract.aspx?paperId=41573}, author = {Alina Sirbu and Heather J Ruskin and Martin Crane} }