References of "Geurts, Pierre"
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See detailCerebral functional connectivity periodically (de)synchronizes with anatomical constraints
Liegeois, Raphaël ULiege; Ziegler, Erik; Bahri, Mohamed Ali ULiege et al

in Brain Structure and Function (2015)

This paper studies the link between resting-state functional connectivity (FC), measured by the correlations of the fMRI BOLD time courses, and structural connectivity (SC), estimated through fiber ... [more ▼]

This paper studies the link between resting-state functional connectivity (FC), measured by the correlations of the fMRI BOLD time courses, and structural connectivity (SC), estimated through fiber tractography. Instead of a static analysis based on the correlation between SC and the FC averaged over the entire fMRI time series, we propose a dynamic analysis, based on the time evolution of the correlation between SC and a suitably windowed FC. Assessing the statistical significance of the time series against random phase permutations, our data show a pronounced peak of significance for time window widths around 20-30 TR (40-60 sec). Using the appropriate window width, we show that FC patterns oscillate between phases of high modularity, primarily shaped by anatomy, and phases of low modularity, primarily shaped by inter-network connectivity. Building upon recent results in dynamic FC, this emphasizes the potential role of SC as a transitory architecture between different highly connected resting state FC patterns. Finally, we show that networks implied in consciousness-related processes, such as the default mode network (DMN), contribute more to these brain-level fluctuations compared to other networks, such as the motor or somatosensory networks. This suggests that the fluctuations between FC and SC are capturing mind-wandering effects. [less ▲]

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See detailClassifying pairs with trees for supervised biological network inference
Schrynemackers, Marie ULiege; Wehenkel, Louis ULiege; Madan Babu, Mohan et al

in Molecular Biosystems (2015), 11(8), 2116-2125

Networks are ubiquitous in biology, and computational approaches have been largely investigated for their inference. In particular, supervised machine learning methods can be used to complete a partially ... [more ▼]

Networks are ubiquitous in biology, and computational approaches have been largely investigated for their inference. In particular, supervised machine learning methods can be used to complete a partially known network by integrating various measurements. Two main supervised frameworks have been proposed: the local approach, which trains a separate model for each network node, and the global approach, which trains a single model over pairs of nodes. Here, we systematically investigate, theoretically and empirically, the exploitation of tree-based ensemble methods in the context of these two approaches for biological network inference. We first formalize the problem of network inference as a classification of pairs, unifying in the process homogeneous and bipartite graphs and discussing two main sampling schemes. We then present the global and the local approaches, extending the latter for the prediction of interactions between two unseen network nodes, and discuss their specializations to tree-based ensemble methods, highlighting their interpretability and drawing links with clustering techniques. Extensive computational experiments are carried out with these methods on various biological networks that clearly highlight that these methods are competitive with existing methods. [less ▲]

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See detailEvaluation and Comparison of Anatomical Landmark Detection Methods for Cephalometric X-Ray Images: A Grand Challenge
Ching Wei, Wang; Cheng-Ta, Huang; Meng-Che, Hsieh et al

in IEEE Transactions on Medical Imaging (2015)

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See detailPhenotype Classification of Zebrafish Embryos by Supervised Learning
Jeanray, Nathalie ULiege; Marée, Raphaël ULiege; Pruvot, Benoist et al

in PLoS ONE (2015), 10(1), 01169891-20

Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances ... [more ▼]

Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100 % agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification. [less ▲]

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See detailErratum: Elevated basal levels of circulating activated platelets predict ICU-acquired sepsis and mortality: a prospective study.
Layios, N.; Delierneux, Céline ULiege; Hego, A. et al

in Critical care (London, England) (2015), 19(1), 301

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See detailErratum: Prospective immune profiling in critically ill adults: before, during and after severe sepsis and septic shock.
Layios, N.; GOSSET, Christian ULiege; Delierneux, Céline ULiege et al

in Critical care (London, England) (2015), 19(1), 300

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See detailA Membrane-Type-1 Matrix Metalloproteinase (MT1-MMP) - Discoidin Domain Receptor 1 Axis Regulates Collagen-Induced Apoptosis in Breast Cancer Cells.
Assent, Delphine; Bourgot, Isabelle ULiege; Hennuy, Benoît ULiege et al

in PloS one (2015), 10(3), 0116006

During tumour dissemination, invading breast carcinoma cells become confronted with a reactive stroma, a type I collagen-rich environment endowed with anti-proliferative and pro-apoptotic properties. To ... [more ▼]

During tumour dissemination, invading breast carcinoma cells become confronted with a reactive stroma, a type I collagen-rich environment endowed with anti-proliferative and pro-apoptotic properties. To develop metastatic capabilities, tumour cells must acquire the capacity to cope with this novel microenvironment. How cells interact with and respond to their microenvironment during cancer dissemination remains poorly understood. To address the impact of type I collagen on the fate of tumour cells, human breast carcinoma MCF-7 cells were cultured within three-dimensional type I collagen gels (3D COL1). Using this experimental model, we have previously demonstrated that membrane type-1 matrix metalloproteinase (MT1-MMP), a proteinase overexpressed in many aggressive tumours, promotes tumour progression by circumventing the collagen-induced up-regulation of BIK, a pro-apoptotic tumour suppressor, and hence apoptosis. Here we performed a transcriptomic analysis to decipher the molecular mechanisms regulating 3D COL1-induced apoptosis in human breast cancer cells. Control and MT1-MMP expressing MCF-7 cells were cultured on two-dimensional plastic plates or within 3D COL1 and a global transcriptional time-course analysis was performed. Shifting the cells from plastic plates to 3D COL1 activated a complex reprogramming of genes implicated in various biological processes. Bioinformatic analysis revealed a 3D COL1-mediated alteration of key cellular functions including apoptosis, cell proliferation, RNA processing and cytoskeleton remodelling. By using a panel of pharmacological inhibitors, we identified discoidin domain receptor 1 (DDR1), a receptor tyrosine kinase specifically activated by collagen, as the initiator of 3D COL1-induced apoptosis. Our data support the concept that MT1-MMP contributes to the inactivation of the DDR1-BIK signalling axis through the cleavage of collagen fibres and/or the alteration of DDR1 receptor signalling unit, without triggering a drastic remodelling of the transcriptome of MCF-7 cells. [less ▲]

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See detailTowards generic image classification: an extensive empirical study
Marée, Raphaël ULiege; Geurts, Pierre ULiege; Wehenkel, Louis ULiege

E-print/Working paper (2014)

This paper considers the general problem of image classification without using any prior knowledge about image classes. We study variants of a method based on supervised learning whose common steps are ... [more ▼]

This paper considers the general problem of image classification without using any prior knowledge about image classes. We study variants of a method based on supervised learning whose common steps are the extraction of random subwindows described by raw pixel intensity values and the use of ensemble of extremely randomized trees to directly classify images or to learn image features. The influence of method parameters and variants is thoroughly evaluated so as to provide baselines and guidelines for future studies. Detailed results are provided on 80 publicly available datasets that depict very diverse types of images (more than 3800 image classes and over 1.5 million images). [less ▲]

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See detailMapping Gene Regulatory Networks in Drosophila Eye Development by Large-Scale Transcriptome Perturbations and Motif Inference
Potier, Delphine; Davie, Kristofer; Hulselmans, Gert et al

in Cell Reports (2014), 9(6), 2290-2303

Genome control is operated by transcription factors (TFs) controlling their target genes by binding to promoters and enhancers. Conceptually, the interactions between TFs, their binding sites, and their ... [more ▼]

Genome control is operated by transcription factors (TFs) controlling their target genes by binding to promoters and enhancers. Conceptually, the interactions between TFs, their binding sites, and their functional targets are represented by gene regulatory networks (GRNs). Deciphering in vivo GRNs underlying organ development in an unbiased genome-wide setting involves identifying both functional TF-gene interactions and physical TF-DNA interactions. To reverse engineer the GRNs of eye development in Drosophila, we performed RNA-seq across 72 genetic perturbations and sorted cell types and inferred a coexpression network. Next, we derived direct TF-DNA interactions using computational motif inference, ultimately connecting 241 TFs to 5,632 direct target genes through 24,926 enhancers. Using this network, we found network motifs, cis-regulatory codes, and regulators of eye development. We validate the predicted target regions of Grainyhead by ChIP-seq and identify this factor as a general cofactor in the eye network, being bound to thousands of nucleosome-free regions. [less ▲]

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See detailRandom forests with random projections of the output space for high dimensional multi-label classification
Joly, Arnaud ULiege; Geurts, Pierre ULiege; Wehenkel, Louis ULiege

in Machine Learning and Knowledge Discovery in Databases (2014, September 15)

We adapt the idea of random projections applied to the out- put space, so as to enhance tree-based ensemble methods in the context of multi-label classification. We show how learning time complexity can ... [more ▼]

We adapt the idea of random projections applied to the out- put space, so as to enhance tree-based ensemble methods in the context of multi-label classification. We show how learning time complexity can be reduced without affecting computational complexity and accuracy of predictions. We also show that random output space projections may be used in order to reach different bias-variance tradeoffs, over a broad panel of benchmark problems, and that this may lead to improved accuracy while reducing significantly the computational burden of the learning stage. [less ▲]

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See detailSimple connectome inference from partial correlation statistics in calcium imaging
Sutera, Antonio ULiege; Joly, Arnaud ULiege; François-Lavet, Vincent ULiege et al

in Soriano, Jordi; Battaglia, Demian; Guyon, Isabelle (Eds.) et al Neural Connectomics Challenge (2014)

In this work, we propose a simple yet effective solution to the problem of connectome inference in calcium imaging data. The proposed algorithm consists of two steps. First, processing the raw signals to ... [more ▼]

In this work, we propose a simple yet effective solution to the problem of connectome inference in calcium imaging data. The proposed algorithm consists of two steps. First, processing the raw signals to detect neural peak activities. Second, inferring the degree of association between neurons from partial correlation statistics. This paper summarises the methodology that led us to win the Connectomics Challenge, proposes a simplified version of our method, and finally compares our results with respect to other inference methods. [less ▲]

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See detailExploiting SNP Correlations within Random Forest for Genome-Wide Association Studies
Botta, Vincent ULiege; Louppe, Gilles ULiege; Geurts, Pierre ULiege et al

in PLoS ONE (2014)

The primary goal of genome-wide association studies (GWAS) is to discover variants that could lead, in isolation or in combination, to a particular trait or disease. Standard approaches to GWAS, however ... [more ▼]

The primary goal of genome-wide association studies (GWAS) is to discover variants that could lead, in isolation or in combination, to a particular trait or disease. Standard approaches to GWAS, however, are usually based on univariate hypothesis tests and therefore can account neither for correlations due to linkage disequilibrium nor for combinations of several markers. To discover and leverage such potential multivariate interactions, we propose in this work an extension of the Random Forest algorithm tailored for structured GWAS data. In terms of risk prediction, we show empirically on several GWAS datasets that the proposed T-Trees method significantly outperforms both the original Random Forest algorithm and standard linear models, thereby suggesting the actual existence of multivariate non-linear effects due to the combinations of several SNPs. We also demonstrate that variable importances as derived from our method can help identify relevant loci. Finally, we highlight the strong impact that quality control procedures may have, both in terms of predictive power and loci identification. [less ▲]

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See detailData normalization and supervised learning to assess the condition of patients with multiple sclerosis based on gait analysis
Azrour, Samir ULiege; Pierard, Sébastien ULiege; Geurts, Pierre ULiege et al

in European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) (2014, April)

Gait impairment is considered as an important feature of disability in multiple sclerosis but its evaluation in the clinical routine remains limited. In this paper, we assess, by means of supervised ... [more ▼]

Gait impairment is considered as an important feature of disability in multiple sclerosis but its evaluation in the clinical routine remains limited. In this paper, we assess, by means of supervised learning, the condition of patients with multiple sclerosis based on their gait descriptors obtained with a gait analysis system. As the morphological characteristics of individuals influence their gait while being in first approximation independent of the disease level, an original strategy of data normalization with respect to these characteristics is described and applied beforehand in order to obtain more reliable predictions. In addition, we explain how we address the problem of missing data which is a common issue in the field of clinical evaluation. Results show that, based on machine learning combined to the proposed data handling techniques, we can predict a score highly correlated with the condition of patients. [less ▲]

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See detailNIMEFI: Gene Regulatory Network Inference using Multiple Ensemble Feature Importance Algorithms
Ruyssinck, Joeri; Huynh-Thu, Vân Anh ULiege; Geurts, Pierre ULiege et al

in PLoS ONE (2014), 9(3), 92709

One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts ... [more ▼]

One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available. [less ▲]

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See detailLong-incubation time-interferon-gamma release assays in response to PPD-, ESAT-6- and/or CFP-10 for the diagnosis of Mycobacterium tuberculosis infection in children
Schepers, Kinda; Mouchet, Françoise; Dirix, Violette et al

in Clinical and Vaccine Immunology (2014), 21(2), 111-118

Background: Diagnosis of childhood active tuberculosis (aTB) or latent Mycobacterium tuberculosis (Mtb) infection (LTBI) remains a challenge, and replacement of tuberculin skin tests (TST) by ... [more ▼]

Background: Diagnosis of childhood active tuberculosis (aTB) or latent Mycobacterium tuberculosis (Mtb) infection (LTBI) remains a challenge, and replacement of tuberculin skin tests (TST) by commercialized interferon-gamma release assays (IGRA) is not currently recommended. Methods: 266 children between 1 month and 15 years of age, 214 being at risk of recent Mtb infection and 51 being included as controls, were prospectively enrolled. According results of clinical evaluation, TST, chest X-Ray and microbiology, children were classified as non-infected, LTBI or aTB. Long-incubation time PPD-, ESAT-6-, and CFP-10-IGRA were performed and evaluated for their accuracy to correctly classify the children. Results: Whereas both TST and PPD-IGRA were suboptimal to detect aTB, combining CFP-10-IGRA with TST or with PPD-IGRA allowed us to detect all the children with aTB, with 96% specificity for children who were positive for CFP-10-IGRA. Moreover, combination of CFP-10- and PPD-IGRA also detected 96% of children classified as LTBI, but a strong IFN-γ response to CFP-10 (>500 pg/ml) was highly suggestive of aTB at least among children less than 3 years old. Conclusions: Long-incubation time CFP-10- and PPD-IGRA should help the clinicians to identify quickly aTB or LTBI in young children. [less ▲]

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See detailBridging physiological and evolutionary time-scales in a gene regulatory network.
Marchand, Gwenaelle; Huynh-Thu, Vân Anh ULiege; Kane, Nolan C. et al

in The New phytologist (2014), 203(2), 685-696

Gene regulatory networks (GRNs) govern phenotypic adaptations and reflect the trade-offs between physiological responses and evolutionary adaptation that act at different time-scales. To identify patterns ... [more ▼]

Gene regulatory networks (GRNs) govern phenotypic adaptations and reflect the trade-offs between physiological responses and evolutionary adaptation that act at different time-scales. To identify patterns of molecular function and genetic diversity in GRNs, we studied the drought response of the common sunflower, Helianthus annuus, and how the underlying GRN is related to its evolution. We examined the responses of 32 423 expressed sequences to drought and to abscisic acid (ABA) and selected 145 co-expressed transcripts. We characterized their regulatory relationships in nine kinetic studies based on different hormones. From this, we inferred a GRN by meta-analyses of a Gaussian graphical model and a random forest algorithm and studied the genetic differentiation among populations (FST ) at nodes. We identified two main hubs in the network that transport nitrate in guard cells. This suggests that nitrate transport is a critical aspect of the sunflower physiological response to drought. We observed that differentiation of the network genes in elite sunflower cultivars is correlated with their position and connectivity. This systems biology approach combined molecular data at different time-scales and identified important physiological processes. At the evolutionary level, we propose that network topology could influence responses to human selection and possibly adaptation to dry environments. [less ▲]

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