References of "Irrthum, Alexandre"
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See detailMyelin-Derived Lipids Modulate Macrophage Activity by Liver X Receptor Activation
Bogie, Jeroen F. J.; Timmermans, Silke; Huynh-Thu, Vân Anh ULg et al

in PLoS ONE (2012), 7(9), 44998

Multiple sclerosis is a chronic, inflammatory, demyelinating disease of the central nervous system in which macrophages and microglia play a central role. Foamy macrophages and microglia, containing ... [more ▼]

Multiple sclerosis is a chronic, inflammatory, demyelinating disease of the central nervous system in which macrophages and microglia play a central role. Foamy macrophages and microglia, containing degenerated myelin, are abundantly found in active multiple sclerosis lesions. Recent studies have described an altered macrophage phenotype after myelin internalization. However, it is unclear by which mechanisms myelin affects the phenotype of macrophages and how this phenotype can influence lesion progression. Here we demonstrate, by using genome wide gene expression analysis, that myelin-phagocytosing macrophages have an enhanced expression of genes involved in migration, phagocytosis and inflammation. Interestingly, myelin internalization also induced the expression of genes involved in liver-X-receptor signaling and cholesterol efflux. In vitro validation shows that myelin-phagocytosing macrophages indeed have an increased capacity to dispose intracellular cholesterol. In addition, myelin suppresses the secretion of the pro-inflammatory mediator IL-6 by macrophages, which was mediated by activation of liver-X-receptor b. Our data show that myelin modulates the phenotype of macrophages by nuclear receptor activation, which may subsequently affect lesion progression in demyelinating diseases such as multiple sclerosis. [less ▲]

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See detailWisdom of crowds for robust gene network inference
Marbach, Daniel; Costello, James C.; Küffner, Robert et al

in Nature Methods (2012), 9

Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a ... [more ▼]

Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data. We characterize the performance, data requirements and inherent biases of different inference approaches, and we provide guidelines for algorithm application and development. We observed that no single inference method performs optimally across all data sets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse data sets. We thereby constructed high-confidence networks for E. coli and S. aureus, each comprising ~ 1,700 transcriptional interactions at a precision of ~50%. We experimentally tested 53 previously unobserved regulatory interactions in E. coli, of which 23 (43%) were supported. Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks. [less ▲]

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See detailInferring Regulatory Networks from Expression Data Using Tree-Based Methods
Huynh-Thu, Vân Anh ULg; Irrthum, Alexandre ULg; Wehenkel, Louis ULg et al

in PLoS ONE (2010), 5(9), 12776

One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high throughput genomic data, in particular microarray ... [more ▼]

One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high throughput genomic data, in particular microarray gene expression data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge aims to evaluate the success of GRN inference algorithms on benchmarks of simulated data. In this article, we present GENIE3, a new algorithm for the inference of GRNs that was best performer in the DREAM4 In Silico Multifactorial challenge. GENIE3 decomposes the prediction of a regulatory network between p genes into p different regression problems. In each of the regression problems, the expression pattern of one of the genes (target gene) is predicted from the expression patterns of all the other genes (input genes), using tree-based ensemble methods Random Forests or Extra-Trees. The importance of an input gene in the prediction of the target gene expression pattern is taken as an indication of a putative regulatory link. Putative regulatory links are then aggregated over all genes to provide a ranking of interactions from which the whole network is reconstructed. In addition to performing well on the DREAM4 In Silico Multifactorial challenge simulated data, we show that GENIE3 compares favorably with existing algorithms to decipher the genetic regulatory network of Escherichia coli. It doesn't make any assumption about the nature of gene regulation, can deal with combinatorial and non-linear interactions, produces directed GRNs, and is fast and scalable. In conclusion, we propose a new algorithm for GRN inference that performs well on both synthetic and real gene expression data. The algorithm, based on feature selection with tree-based ensemble methods, is simple and generic, making it adaptable to other types of genomic data and interactions. [less ▲]

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See detailSupervised learning with decision tree-based methods in computational and systems biology
Geurts, Pierre ULg; Irrthum, Alexandre ULg; Wehenkel, Louis ULg

in Molecular Biosystems (2009), 5(12), 1593-1605

At the intersection between artificial intelligence and statistics, supervised learning provides algorithms to automatically build predictive models only from observations of a system. During the last ... [more ▼]

At the intersection between artificial intelligence and statistics, supervised learning provides algorithms to automatically build predictive models only from observations of a system. During the last twenty years, supervised learning has been a tool of choice to analyze the always increasing and complexifying data generated in the context of molecular biology, with successful applications in genome annotation, function prediction, or biomarker discovery. Among supervised learning methods, decision tree-based methods stand out as non parametric methods that have the unique feature of combining interpretability, efficiency, and, when used in ensembles of trees, excellent accuracy. The goal of this paper is to provide an accessible and comprehensive introduction to this class of methods. The first part of the paper is devoted to an intuitive but complete description of decision tree-based methods and a discussion of their strengths and limitations with respect to other supervised learning methods. The second part of the paper provides a survey of their applications in the context of computational and systems biology. The supplementary material provides information about various non-standard extensions of the decision tree-based approach to modeling, some practical guidelines for the choice of parameters and algorithm variants depending on the practical ob jectives of their application, pointers to freely accessible software packages, and a brief primer going through the different manipulations needed to use the tree-induction packages available in the R statistical tool. [less ▲]

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See detailPredicting gene essentiality from expression patterns in Escherichia coli
Irrthum, Alexandre ULg; Wehenkel, Louis ULg

(2008)

Essential genes are genes whose loss of function causes lethal- ity. In the case of pathogen organisms, the identification of these genes is of considerable interest, as they provide targets for the ... [more ▼]

Essential genes are genes whose loss of function causes lethal- ity. In the case of pathogen organisms, the identification of these genes is of considerable interest, as they provide targets for the development of novel antibiotics. Computational analyses have revealed that the posi- tions of the encoded proteins in the protein-protein interaction network can help predict essentiality, but this type of data is not always avail- able. In this work, we investigate prediction of gene essentiality from expression data only, using a genome-wide compendium of expression patterns in the bacterium Escherichia coli, by using single decision trees and random forests. We first show that, based on the original expression measurements, it is possible to identify essential genes with good accu- racy. Next, we derive, for each gene, higher level features such as average, standard deviation and entropy of its expression pattern, as well as fea- tures related to the correlation of expression patterns between genes. We find that essentiality may actually be predicted based only on the two most relevant ones among these latter.We discuss the biological meaning of these observations. [less ▲]

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