References of "Glaab, Enrico 50001863"
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See detailOn different aspects of network analysis in systems biology
Chaiboonchoe, Amphun; Jurkowski, Wiktor UL; Pellet, Johann et al

in Systems Biology (2013), 1

Network analysis is an essential component of systems biology approaches toward understanding the molecular and cellular interactions underlying biological systems functionalities and their perturbations ... [more ▼]

Network analysis is an essential component of systems biology approaches toward understanding the molecular and cellular interactions underlying biological systems functionalities and their perturbations in disease. Regulatory and signalling pathways involve DNA, RNA, proteins and metabolites as key elements to coordinate most aspects of cellular functioning. Cellular processes depend on the structure and dynamics of gene regulatory networks and can be studied by employing a network representation of molecular interactions. This chapter describes several types of biological networks, how combination of different analytic approaches can be used to study diseases, and provides a list of selected tools for network visualization and analysis. It also introduces protein-protein interaction networks, gene regulatory networks, signalling networks and metabolic networks to illustrate concepts underlying network representation of cellular processes and molecular interactions. It finally discusses how the level of accuracy in inferring functional relationships influences the choice of methods applied for the analysis of a particular biological network type. © Springer Science+Business Media Dordrecht 2013. All rights are reserved. [less ▲]

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See detailNetwork deregulation analysis in complex diseases via the pairwise elastic net
Vlassis, Nikos UL; Glaab, Enrico UL

in Proc 8th BeNeLux Bioinformatics Conference (2013)

Complex diseases like neurodegenerative or cancer disorders are characterized by deregulations in multiple genes and proteins. Previous research has shown that neighboring genes in a molecular network ... [more ▼]

Complex diseases like neurodegenerative or cancer disorders are characterized by deregulations in multiple genes and proteins. Previous research has shown that neighboring genes in a molecular network tend to undergo coordinated expression changes. We describe an approach that allows identifying such jointly differentially expressed genes from input expression data and a graph encoding pairwise functional associations between genes (such as protein interactions). We cast this as a feature selection problem in penalized two-class (cases vs. controls) classification, and we propose a novel Pairwise Elastic Net penalty that favors the selection of discriminative genes according to their connectedness in the interaction graph. Experiments on microarray gene expression data for Parkinson’s disease demonstrate marked improvements in feature grouping over competitive methods. [less ▲]

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See detailCondensing the omics fog of microbial communities
Muller, Emilie UL; Glaab, Enrico UL; May, Patrick UL et al

in Trends in Microbiology (2013), 21(7), 325333

Natural microbial communities are ubiquitous, complex, heterogeneous and dynamic. Here, we argue that the future standard for their study will require systematic omic measurements of spatially and ... [more ▼]

Natural microbial communities are ubiquitous, complex, heterogeneous and dynamic. Here, we argue that the future standard for their study will require systematic omic measurements of spatially and temporally resolved unique samples in line with a discovery-driven planning approach. Resulting datasets will allow the generation of solid hypotheses about causal relationships and, thereby, will facilitate the discovery of previously unknown traits of specific microbial community members. However, to achieve this, solid wet-lab, bioinformatic and statistical methodologies are required to have the promises of the emerging field of Eco-Systems Biology come to fruition. [less ▲]

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See detailFunctional Genomics, Proteomics, Metabolomics and Bioinformatics for Systems Biology
Ballereau, S.; Glaab, Enrico UL; Kolodkin, Alexey UL et al

in Prokop, Ales; Csukás, Bela (Eds.) Systems Biology: Integrative Biology and Simulation Tools (2013)

This chapter introduces systems biology, its context, aims, concepts and strategies. It then describes approaches and methods used for collection of high-dimensional structural and functional genomics ... [more ▼]

This chapter introduces systems biology, its context, aims, concepts and strategies. It then describes approaches and methods used for collection of high-dimensional structural and functional genomics data, including epigenomics, transcriptomics, proteomics, metabolomics and lipidomics, and how recent technological advances in these fields have moved the bottleneck from data production to data analysis and bioinformatics. Finally, the most advanced mathematical and computational methods used for clustering, feature selection, prediction analysis, text mining and pathway analysis in functional genomics and systems biology are reviewed and discussed in the context of use cases. [less ▲]

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See detailNetwork analysis for systems biology
Chaiboonchoe, A.; Jurkowski, Wiktor UL; Pellet, J. et al

in Prokop, Aleš; Csukás (Eds.) Springer book in Systems Biology, Vol.1: Systems Biology:, Integrative Biology and Simulation Tools (2013)

Network analysis is an essential component of systems biology approaches toward understanding the molecular and cellular interactions underlying biological systems functionalities and their perturbations ... [more ▼]

Network analysis is an essential component of systems biology approaches toward understanding the molecular and cellular interactions underlying biological systems functionalities and their perturbations in disease. Regulatory and signalling pathways, which involve DNA, RNA proteins and metabolites as key elements, coordinate most aspects of cellular functioning. Cellular processes, which are dependent on the structure and dynamics of gene regulatory networks, can be studied by employing a network representation of molecular interactions. In this chapter we describe several types of networks and how combination of different analytic approaches can be used to study diseases. We provide a list of selected tools for visualization and network analysis. We introduce protein-protein interaction networks, gene regulatory networks, signalling networks and metabolic networks. We then define concepts underlying network representation of cellular processes and molecular interactions. We finally discuss how the level of accuracy in inferring functional relationships influences the choice of methods applied for the analysis of a particular network type. [less ▲]

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See detailUsing rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data
Glaab, Enrico UL; Bacardit, Jaume; Garibaldi, Jonathan M. et al

in PLoS ONE (2012), 7(7), 39932-39932

Microarray data analysis has been shown to provide an effective tool for studying cancer and genetic diseases. Although classical machine learning techniques have successfully been applied to find ... [more ▼]

Microarray data analysis has been shown to provide an effective tool for studying cancer and genetic diseases. Although classical machine learning techniques have successfully been applied to find informative genes and to predict class labels for new samples, common restrictions of microarray analysis such as small sample sizes, a large attribute space and high noise levels still limit its scientific and clinical applications. Increasing the interpretability of prediction models while retaining a high accuracy would help to exploit the information content in microarray data more effectively. For this purpose, we evaluate our rule-based evolutionary machine learning systems, BioHEL and GAssist, on three public microarray cancer datasets, obtaining simple rule-based models for sample classification. A comparison with other benchmark microarray sample classifiers based on three diverse feature selection algorithms suggests that these evolutionary learning techniques can compete with state-of-the-art methods like support vector machines. The obtained models reach accuracies above 90% in two-level external cross-validation, with the added value of facilitating interpretation by using only combinations of simple if-then-else rules. As a further benefit, a literature mining analysis reveals that prioritizations of informative genes extracted from BioHEL’s classification rule sets can outperform gene rankings obtained from a conventional ensemble feature selection in terms of the pointwise mutual information between relevant disease terms and the standardized names of top-ranked genes. [less ▲]

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See detailEnrichNet: network-based gene set enrichment analysis
Glaab, Enrico UL; Baudot, A.; Krasnogor, N. et al

in Bioinformatics (2012), 28(18), 451-457

Assessing functional associations between an experimentally derived gene or protein set of interest and a database of known gene/protein sets is a common task in the analysis of large-scale functional ... [more ▼]

Assessing functional associations between an experimentally derived gene or protein set of interest and a database of known gene/protein sets is a common task in the analysis of large-scale functional genomics data. For this purpose, a frequently used approach is to apply an over-representation-based enrichment analysis. However, this approach has four drawbacks: (i) it can only score functional associations of overlapping gene/proteins sets; (ii) it disregards genes with missing annotations; (iii) it does not take into account the network structure of physical interactions between the gene/protein sets of interest and (iv) tissue-specific gene/protein set associations cannot be recognized. RESULTS: To address these limitations, we introduce an integrative analysis approach and web-application called EnrichNet. It combines a novel graph-based statistic with an interactive sub-network visualization to accomplish two complementary goals: improving the prioritization of putative functional gene/protein set associations by exploiting information from molecular interaction networks and tissue-specific gene expression data and enabling a direct biological interpretation of the results. By using the approach to analyse sets of genes with known involvement in human diseases, new pathway associations are identified, reflecting a dense sub-network of interactions between their corresponding proteins. [less ▲]

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See detailPathVar: analysis of gene and protein expression variance in cellular pathways using microarray data
Glaab, Enrico UL; Schneider, Reinhard UL

in Bioinformatics (2012)

Finding significant differences between the expression levels of genes or proteins across diverse biological conditions is one of the primary goals in the analysis of functional genomics data. However ... [more ▼]

Finding significant differences between the expression levels of genes or proteins across diverse biological conditions is one of the primary goals in the analysis of functional genomics data. However, existing methods for identifying differentially expressed genes or sets of genes by comparing measures of the average expression across predefined sample groups do not detect differential variance in the expression levels across genes in cellular pathways. Since corresponding pathway deregulations occur frequently in microarray gene or protein expression data, we present a new dedicated web application, PathVar, to analyze these data sources. The software ranks pathway-representing gene/protein sets in terms of the differences of the variance in the within-pathway expression levels across different biological conditions. Apart from identifying new pathway deregulation patterns, the tool exploits these patterns by combining different machine learning methods to find clusters of similar samples and build sample classification models. [less ▲]

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See detailA genome-wide network model capturing seed germination reveals co-ordinated regulation of plant cellular phase transitions
Bassel, George W.; Lanc, Hui; Glaab, Enrico UL et al

in Proceedings of the National Academy of Sciences of the United States of America (2011), 108(23), 9709-9714

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See detailRERG (Ras-like, oestrogen-regulated, growth-inhibitor) expression in breast cancer: a marker of ER-positive luminal-like subtype
Habashy, Hany O.; Powe, Desmond G.; Glaab, Enrico UL et al

in Breast Cancer Research and Treatment (2011), 128(2), 315-326

Global gene expression profiling studies have classified breast cancer into a number of distinct biological and molecular classes with clinical relevance. The heterogeneous luminal group, which is largely ... [more ▼]

Global gene expression profiling studies have classified breast cancer into a number of distinct biological and molecular classes with clinical relevance. The heterogeneous luminal group, which is largely characterised by oestrogen receptor (ER) expression, appears to contain distinct subgroups with differing behaviour. In this study, we analysed 47,293 gene transcripts in 128 invasive breast carcinomas (BC) using Artificial Neural Networks and a cross-validation analysis in combination with an ensemble sample classification to identify genes that can be used to subclassify ER+ luminal tumours. The results were validated using immunohistochemistry on TMAs containing 1,140 invasive breast cancers. Our results showed that the RERG gene is one of the highest ranked genes to differentiate between ER+ luminal-like and ER- non-luminal cancers based on a 10-fold external cross-validation analysis with an average classification accuracy of 89%. This was confirmed in our protein expression studies that showed RERG positive associations with markers of luminal differentiation including ER, luminal cytokeratins (CK19, CK18 and CK7/8) and FOXA1 (P = 0.004) and other markers of good prognosis in BC including small size, lower histologic grade and positive expression of androgen receptor, nuclear BRCA1, FHIT and cell cycle inhibitors p27 and p21. RERG expression was inversely associated with the proliferation marker MIB1 (P = 0.005) and p53. Strong RERG expression showed an association with longer breast cancer specific survival and distant metastasis free interval in the whole series as well as in the ER+ luminal group and these associations were independent of other prognostic variables. In conclusion, we used novel bioinformatics methods to identify candidate genes to characterise ER+ luminal-like breast cancer. RERG gene is a key marker of the luminal BC class and can be used to separate distinct prognostic subgroups. [less ▲]

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See detailFunctional Network Construction in Arabidopsis Using Rule-Based Machine Learning on Large-Scale Data Sets
Bassel, George W.; Glaab, Enrico UL; Marquez, Julietta et al

in Plant Cell (2011)

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See detailA low protein diet during early gestation in sheep detrimentally impacts hepatic glucose metabolism in the adult offspring
Gardner, David S.; Rhodes, Phillip; Karamitri, Angeliki et al

in Proceedings of the Nutrition Society 2011 (2011)

Differences in maternal diet can account for variation in the metabolic competence of the subsequent individual as an adult. ‘Developmental programming’ may impair fetal organ development leading to a ... [more ▼]

Differences in maternal diet can account for variation in the metabolic competence of the subsequent individual as an adult. ‘Developmental programming’ may impair fetal organ development leading to a limitation in function as an adult and/or increase the rate of age-related organ decline for example under conditions of obesity. Here, we have tested the interaction between prenatal nutritional ‘thrift’ and postnatal nutritional excess on gluco-regulatory functions in an ovine model. Seventy-four Scottish Blackface ewes were randomly assigned to receive either a control protein diet with adequate energy (18% protein; CP, n 20) or low protein diet (9% protein) fed during early gestation (0–65 d, term ~147 d; LPE, n 37) or late gestation (65–147 d; LPL, n 17). At 65 d a proportion of ewes was euthanised for fetal sampling. At term, remaining ewes lambed naturally, were weaned at 10 weeks and a random sample of offspring studied longitudinally when lean (1.5 years of age) and after 6 months exposure to an obesogenic environment. Body composition was determined by dual-energy absorptiometry and glucose and insulin tolerance tests were conducted with appropriate sampling intervals. At post mortem, muscle and hepatic tissues were sampled for expression and abundance of relevant gluco-regulatory genes. The diets had little effect on maternal weight and body composition through gestation or on fetal weights at 65 d. Term weight was reduced by ~500 g (P = 0.001) in LPL v. other groups but, by weaning, body weight was similar between groups and growth rate to adulthood was not different. Homeostasis model assessment of baseline glucose and insulin concentrations indicated relative insulin resistance in male LPE . Indeed, when challenged with a GTT, the incremental insulin AUC was significantly greater in male LPE when obese but not when lean (unpublished results). Molecular quantification of glucose-insulin pathways in muscle and liver indicated specific down-regulation of the hepatic insulin, but not lipid, pathways in male liver only. Muscle insulin-signalling pathways were unaffected as determined by microarray (Affymetrix, U133 chip; www.arraymining.net). The data suggest that a maternal, low protein, diet during early gestation specifically impacts upon the function of the resulting adult liver, such that the offspring appear more susceptible to large excursions in plasma insulin during gluco-regulatory challenges. The insulin sensitivity of offspring muscle, the largest single source of insulin-stimulated glucose uptake, was largely unaffected. Thus, obesity appears to exacerbate any functional deficits inherent in low protein exposed offspring in sheep, but those offspring born of low birth weight were largely unaffected, illustrating that nutritional quality is far more important than nutritional quantity especially during sensitive developmental phases of growth. [less ▲]

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See detailLearning pathway-based decision rules to classify microarray cancer samples
Glaab, Enrico UL; Garibaldi, Jonathan M.; Krasnogor, N.

in German Conference on Bioinformatics 2010, Lecture Notes in Informatics (LNI) (2010)

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See detailVRMLGen: An R package for 3D Data Visualization on the Web
Glaab, Enrico UL; Garibaldi, Jonathan M.; Krasnogor, Natalio

in Journal of Statistical Software (2010), 36(8), 1-18

The 3-dimensional representation and inspection of complex data is a frequently used strategy in many data analysis domains. Existing data mining software often lacks functionality that would enable users ... [more ▼]

The 3-dimensional representation and inspection of complex data is a frequently used strategy in many data analysis domains. Existing data mining software often lacks functionality that would enable users to explore 3D data interactively, especially if one wishes to make dynamic graphical representations directly viewable on the web. In this paper we present vrmlgen, a software package for the statistical programming language R to create 3D data visualizations in web formats like the Virtual Reality Markup Language (VRML) and LiveGraphics3D. vrmlgen can be used to generate 3D charts and bar plots, scatter plots with density estimation contour surfaces, and visualizations of height maps, 3D object models and parametric functions. For greater flexibility, the user can also access low-level plotting methods through a unified interface and freely group different function calls together to create new higher-level plotting methods. Additionally, we present a web tool allowing users to visualize 3D data online and test some of vrmlgen's features without the need to install any software on their computer. [less ▲]

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See detailCombining chondrocyte gene expression, literature mining and pathway/network analysis to extract biological insights from small-scale microarray data
Glaab, Enrico UL; Clutterbuck, L.; Bacardit, J. et al

in Osteoarthritis and Cartilage (2010), 18(2), 169

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See detailLuminal-like oestrogen receptor-positive breast cancer: identification of prognostic biological subclasses
Habashy, H.O.; Powe, D.G.; Ball, G. et al

in European Journal of Cancer Supplements (2010), 8(3), 91

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See detailExtending pathways and processes using molecular interaction networks to analyse cancer genome data
Glaab, Enrico UL; Baudot, Anais; Krasnogor, Natalio et al

in BMC Bioinformatics (2010), 11(1), 597-597

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See detailTopoGSA: network topological gene set analysis
Glaab, Enrico UL; Baudot, Anais; Krasnogor, Natalio et al

in Bioinformatics (2010), 26(9), 1271-1272

TopoGSA (Topology-based Gene Set Analysis) is a web-application dedicated to the computation and visualization of network topological properties for gene and protein sets in molecular interaction networks ... [more ▼]

TopoGSA (Topology-based Gene Set Analysis) is a web-application dedicated to the computation and visualization of network topological properties for gene and protein sets in molecular interaction networks. Different topological characteristics, such as the centrality of nodes in the network or their tendency to form clusters, can be computed and compared with those of known cellular pathways and processes. [less ▲]

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See detailArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization
Glaab, Enrico UL; Garibaldi, Jonathan M.; Krasnogor, N.

in BMC Bioinformatics (2009), 10(1), 358-358

Background: Statistical analysis of DNA microarray data provides a valuable diagnostic tool for the investigation of genetic components of diseases. To take advantage of the multitude of available data ... [more ▼]

Background: Statistical analysis of DNA microarray data provides a valuable diagnostic tool for the investigation of genetic components of diseases. To take advantage of the multitude of available data sets and analysis methods, it is desirable to combine both different algorithms and data from different studies. Applying ensemble learning, consensus clustering and cross-study normalization methods for this purpose in an almost fully automated process and linking different analysis modules together under a single interface would simplify many microarray analysis tasks. Results: We present ArrayMining.net, a web-application for microarray analysis that provides easy access to a wide choice of feature selection, clustering, prediction, gene set analysis and cross-study normalization methods. In contrast to other microarray-related web-tools, multiple algorithms and data sets for an analysis task can be combined using ensemble feature selection, ensemble prediction, consensus clustering and cross-platform data integration. By interlinking different analysis tools in a modular fashion, new exploratory routes become available, e.g. ensemble sample classification using features obtained from a gene set analysis and data from multiple studies. The analysis is further simplified by automatic parameter selection mechanisms and linkage to web tools and databases for functional annotation and literature mining. Conclusion: ArrayMining.net is a free web-application for microarray analysis combining a broad choice of algorithms based on ensemble and consensus methods, using automatic parameter selection and integration with annotation databases. [less ▲]

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