00450nas a2200133 4500008004100000245008600041210006900127300001400196490000600210100001700216700001800233700002300251856004200274 2015 eng d00aData Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks0 aData Integration for Microarrays Enhanced Inference for Gene Reg a255–2690 v41 aSirbu, Alina1 aCrane, Martin1 aRuskin, Heather, J uhttp://www.mdpi.com/2076-3905/4/2/25500512nas a2200133 4500008004100000245009000041210007100131260003800202300001400240100001700254700001800271700002300289856006600312 2014 eng d00aEGIA–Evolutionary Optimisation of Gene Regulatory Networks, an Integrative Approach0 aEGIA–Evolutionary Optimisation of Gene Regulatory Networks an In bSpringer International Publishing a217–2291 aSirbu, Alina1 aCrane, Martin1 aRuskin, Heather, J uhttp://link.springer.com/chapter/10.1007/978-3-319-05401-8_2101654nas a2200169 4500008004100000022001400041245009000055210006900145260001300214300001100227490000800238520105800246100001701304700002301321700001801344856012201362 2012 eng d a1611-753000aIntegrating heterogeneous gene expression data for gene regulatory network modelling.0 aIntegrating heterogeneous gene expression data for gene regulato c2012 Jun a95-1020 v1313 a
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' 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.
1 aSirbu, Alina1 aRuskin, Heather, J1 aCrane, Martin uhttps://kdd.isti.cnr.it/publications/integrating-heterogeneous-gene-expression-data-gene-regulatory-network-modelling02529nas a2200181 4500008004100000022001400041245012200055210006900177260000900246300001100255490000600266520187200272100001702144700001902161700001802180700002302198856012602221 2012 eng d a1932-620300aRNA-Seq vs dual- and single-channel microarray data: sensitivity analysis for differential expression and clustering.0 aRNASeq vs dual and singlechannel microarray data sensitivity ana c2012 ae509860 v73 aWith 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'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.
1 aSirbu, Alina1 aKerr, Gráinne1 aCrane, Martin1 aRuskin, Heather, J uhttps://kdd.isti.cnr.it/publications/rna-seq-vs-dual-and-single-channel-microarray-data-sensitivity-analysis-differential02085nas a2200805 4500008004100000245005500041210005500096300001200151490000600163100002000169700001900189700002100208700001500229700001600244700001800260700001800278700002000296700001800316700001700334700002500351700001600376700001300392700001600405700001800421700002000439700001400459700001700473700001500490700002100505700001800526700001500544700001900559700002000578700001700598700001500615700001600630700001700646700002000663700001600683700001800699700001400717700001500731700001400746700001300760700001700773700001500790700001500805700001500820700001400835700001400849700001900863700001400882700002300896700001400919700001500933700001700948700001300965700001600978700001800994700001501012700002001027700001601047700001701063700001801080700001701098700001801115700001401133700001501147856011701162 2012 eng d00aWisdom of crowds for robust gene network inference0 aWisdom of crowds for robust gene network inference a796-8040 v91 aMarbach, Daniel1 aCostello, J.C.1 aKüffner, Robert1 aVega, N.M.1 aPrill, R.J.1 aCamacho, D.M.1 aAllison, K.R.1 aKellis, Manolis1 aCollins, J.J.1 aAderhold, A.1 aStolovitzky, Gustavo1 aBonneau, R.1 aChen, Y.1 aCordero, F.1 aCrane, Martin1 aDondelinger, F.1 aDrton, M.1 aEsposito, R.1 aFoygel, R.1 aDe La Fuente, A.1 aGertheiss, J.1 aGeurts, P.1 aGreenfield, A.1 aGrzegorczyk, M.1 aHaury, A.-C.1 aHolmes, B.1 aHothorn, T.1 aHusmeier, D.1 aHuynh-Thu, V.A.1 aIrrthum, A.1 aKarlebach, G.1 aLebre, S.1 aDe Leo, V.1 aMadar, A.1 aMani, S.1 aMordelet, F.1 aOstrer, H.1 aOuyang, Z.1 aPandya, R.1 aPetri, T.1 aPinna, A.1 aPoultney, C.S.1 aRezny, S.1 aRuskin, Heather, J1 aSaeys, Y.1 aShamir, R.1 aSirbu, Alina1 aSong, M.1 aSoranzo, N.1 aStatnikov, A.1 aVega, N.M.1 aVera-Licona, P.1 aVert, J.-P.1 aVisconti, A.1 aWang, Haizhou1 aWehenkel, L.1 aWindhager, L.1 aZhang, Y.1 aZimmer, R. uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84870305264&partnerID=40&md5=04a686572bdefff60157bf68c95df7ea00508nas a2200121 4500008004100000245008100041210006900122260001100191100001700202700002300219700001800242856012600260 2011 eng d00aStages of Gene Regulatory Network Inference: the Evolutionary Algorithm Role0 aStages of Gene Regulatory Network Inference the Evolutionary Alg bInTech1 aSirbu, Alina1 aRuskin, Heather, J1 aCrane, Martin uhttp://www.intechopen.com/articles/show/title/stages-of-gene-regulatory-network-inference-the-evolutionary-algorithm-role01924nas a2200169 4500008004100000022001400041245008600055210006900141260000900210300000700219490000700226520134700233100001701580700002301597700001801620856011601638 2010 eng d a1471-210500aComparison of evolutionary algorithms in gene regulatory network model inference.0 aComparison of evolutionary algorithms in gene regulatory network c2010 a590 v113 aBACKGROUND: 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.
1 aSirbu, Alina1 aRuskin, Heather, J1 aCrane, Martin uhttps://kdd.isti.cnr.it/publications/comparison-evolutionary-algorithms-gene-regulatory-network-model-inference01557nas a2200169 4500008004100000022001400041245008300055210006900138260000900207300001100216490000600227520098100233100001701214700002301231700001801254856011501272 2010 eng d a1932-620300aCross-platform microarray data normalisation for regulatory network inference.0 aCrossplatform microarray data normalisation for regulatory netwo c2010 ae138220 v53 aBACKGROUND: 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.
1 aSirbu, Alina1 aRuskin, Heather, J1 aCrane, Martin uhttps://kdd.isti.cnr.it/publications/cross-platform-microarray-data-normalisation-regulatory-network-inference00450nas a2200121 4500008004100000245008700041210006900128300001600197100001700213700002300230700001800253856005700271 2010 eng d00aRegulatory network modelling: Correlation for structure and parameter optimisation0 aRegulatory network modelling Correlation for structure and param a3473–34811 aSirbu, Alina1 aRuskin, Heather, J1 aCrane, Martin uhttp://www.actapress.com/Abstract.aspx?paperId=41573