References of "Wehenkel, Louis"
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See detailLazy planning under uncertainty by optimizing decisions on an ensemble of incomplete disturbance trees
Defourny, Boris ULg; Ernst, Damien ULg; Wehenkel, Louis ULg

in Defourny, Boris; Ernst, Damien; Wehenkel, Louis (Eds.) Recent Advances in Reinforcement Learning (2008)

This paper addresses the problem of solving discrete-time optimal sequential decision making problems having a disturbance space W composed of a finite number of elements. In this context, the problem of ... [more ▼]

This paper addresses the problem of solving discrete-time optimal sequential decision making problems having a disturbance space W composed of a finite number of elements. In this context, the problem of finding from an initial state x0 an optimal decision strategy can be stated as an optimization problem which aims at finding an optimal combination of decisions attached to the nodes of a disturbance tree modeling all possible sequences of disturbances w0, w1, . . ., w(T−1) in W^T over the optimization horizon T. A significant drawback of this approach is that the resulting optimization problem has a search space which is the Cartesian product of O(|W|^(T−1)) decision spaces U, which makes the approach computationally impractical as soon as the optimization horizon grows, even if W has just a handful of elements. To circumvent this difficulty, we propose to exploit an ensemble of randomly generated incomplete disturbance trees of controlled complexity, to solve their induced optimization problems in parallel, and to combine their predictions at time t = 0 to obtain a (near-)optimal first-stage decision. Because this approach postpones the determination of the decisions for subsequent stages until additional information about the realization of the uncertain process becomes available, we call it lazy. Simulations carried out on a robot corridor navigation problem show that even for small incomplete trees, this approach can lead to near-optimal decisions. [less ▲]

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See detailExploiting tree-based variable importances to selectively identify relevant variables
Huynh-Thu, Vân Anh; Wehenkel, Louis ULg; Geurts, Pierre ULg

in Proc. of FSDM08, ECML/PKDD Workshop on New challenges for feature selection in data mining and knowledge discovery (2008)

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See detailNew challenges for feature selection in data mining and knowledge discovery
Saeys, Yvan; Liu, Huan; Inza, Inaki et al

in Journal of Machine Learning Research (2008), 4

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See detailEstimation de densité par ensemble aléatoire de poly-arbres
Ammar, Sourour; Leray, Philippe; Wehenkel, Louis ULg

(2008)

La notion de mélange de modèles simples aléatoires est de plus en plus utilisée et avec succès dans la littérature de l’apprentissage supervisé ces dernières années. Parmi les avantages de ces méthodes ... [more ▼]

La notion de mélange de modèles simples aléatoires est de plus en plus utilisée et avec succès dans la littérature de l’apprentissage supervisé ces dernières années. Parmi les avantages de ces méthodes, citons l’amélioration du passage à l’échelle des algorithmes d’apprentissage grâce à leur aspect aléatoire et l’amélioration de l’exactitude de la prédiction des modèles induits grâce à une flexibilité plus élevée en ce qui concerne le compromis biais/variance. Dans le présent travail, nous proposons d’explorer cette idée dans le contexte de l’estimation de la densité. Nous proposons une nouvelle famille de méthodes d’apprentissage non-supervisé à base de mélange de grands ensembles aléatoires de poly-arbres. La caractéristique spécifique de ces méthodes est leur passage à l’échelle, aussi bien en terme de nombre de variables que de données à traiter. Cette étude, exploratoire, compare empiriquement ces méthodes sur un ensemble de problèmes de test discrets de taille et de complexité croissantes et ouvre de nombreuses perspectives auxquelles nous prévoyons de nous intéresser. [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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See detailCompositional protein analysis of HDL by SELDI-TOF MS during experimental endotoxemia
Levels, Johannes HM; Marée, Raphaël ULg; Geurts, Pierre ULg et al

Poster (2008)

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See detailHigh-dimensional probability density estimation with randomized ensembles of tree structured bayesian networks
Ammar, Sourour; Leray, Philippe; Defourny, Boris ULg et al

in Proc. of the 4th European Workshop on Probabilistic Graphical Models (PGM08) (2008)

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See detailPREDetector : Prokaryotic Regulatory Element Detector
Hiard, Samuel ULg; Rigali, Sébastien ULg; Colson, Séverine ULg et al

Poster (2007, November 12)

Background: In the post-genomic area, in silico predictions of regulatory networks are considered as a powerful approach to decipher and understand biological pathways within prokaryotic cells. The ... [more ▼]

Background: In the post-genomic area, in silico predictions of regulatory networks are considered as a powerful approach to decipher and understand biological pathways within prokaryotic cells. The emergence of position weight matrices based programs has facilitated the access to this approach. However, a tool that automatically estimates the reliability of the predictions and would allow users to extend predictions in genomic regions generally regarded with no regulatory functions was still highly demanded. Result: Here, we introduce PREDetector, a tool developed for predicting regulons of DNA-binding proteins in prokaryotic genomes that (i) automatically predicts, scores and positions potential binding sites and their respective target genes, (ii) includes the downstream co-regulated genes, (iii) extends the predictions to coding sequences and terminator regions, (iv) saves private matrices and allows predictions in other genomes, and (v) provides an easy way to estimate the reliability of the predictions. Conclusion: We present, with PREDetector, an accurate prokaryotic regulon prediction tool that maximally answers biologists’ requests. PREDetector can be downloaded freely at http://www.montefiore.ulg.ac.be/~hiard/predetectorfr.html [less ▲]

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See detailContingency filtering techniques for preventive security-constrained optimal power flow
Capitanescu, Florin ULg; Glavic, M.; Ernst, Damien ULg et al

in IEEE Transactions on Power Systems (2007), 22(4), 1690-1697

This paper focuses on contingency filtering to accelerate the iterative solution of preventive security-constrained optimal power flow (PSCOPF) problems. To this end, we propose two novel filtering ... [more ▼]

This paper focuses on contingency filtering to accelerate the iterative solution of preventive security-constrained optimal power flow (PSCOPF) problems. To this end, we propose two novel filtering techniques relying on the comparison at an intermediate PSCOPF solution of post-contingency constraint violations among postulated contingencies. We assess these techniques by comparing them with severity index-based filtering schemes, on a 60- and a 118-bus system. Our results show that the proposed contingency filtering techniques lead to faster solution of the PSCOPF, while being more robust and meaningful, than severity-index based ones. [less ▲]

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See detailEstimation of rotor angles of synchronous machines using artificial neural networks and local PMU-based quantities
Del Angel, A.; Geurts, Pierre ULg; Ernst, Damien ULg et al

in Neurocomputing (2007), 70(16-18), 2668-2678

This paper investigates a possibility for estimating rotor angles in the time frame of transient (angle) stability of electric power systems, for use in real-time. The proposed dynamic state estimation ... [more ▼]

This paper investigates a possibility for estimating rotor angles in the time frame of transient (angle) stability of electric power systems, for use in real-time. The proposed dynamic state estimation technique is based on the use of voltage and current phasors obtained from a phasor measurement unit supposed to be installed on the extra-high voltage side of the substation of a power plant, together with a multilayer perceptron trained off-line from simulations. We demonstrate that an intuitive approach to directly map phasor measurement inputs to the neural network to generator rotor angle does not offer satisfactory results. We found out that a good way to approach the angle estimation problem is to use two neural networks in order to estimate the sin(delta) and cos(delta) of the angle and recover the latter from these values by simple post-processing. Simulation results on a part of the Mexican interconnected system show that the approach could yield satisfactory accuracy for realtime monitoring and control of transient instability. (c) 2007 Elsevier B.V. All rights reserved. [less ▲]

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See detailDetection of micro-RNA/gene interactions involved in angiogenesis using machine learning techniques
Huynh-Thu, Vân Anh ULg; Hiard, Samuel ULg; Geurts, Pierre ULg et al

Poster (2007, September)

Motivation: Angiogenesis is the process responsible for the growth of new blood vessels from existing ones. It is also associated with the development of cancer, as tumors need to be irrigated by blood ... [more ▼]

Motivation: Angiogenesis is the process responsible for the growth of new blood vessels from existing ones. It is also associated with the development of cancer, as tumors need to be irrigated by blood vessels for growing. New cancer therapies appear that exploit angiogenesis inhibitors, also called angiostatic agents, to asphyxiate and starve the tumors. Better understanding the regulatory mechanisms that control angiogenesis is thus fundamental. Recently, short non-coding RNA molecules, called micro-RNAs, have been discovered that are involved in post- transcriptional regulation of gene expressions. These molecules bind to RNA messengers following the base pairing rules, preventing them from being translated into proteins and/or tagging them for degradation. The main goal of this work is to use computational approaches to identify micro-RNAs involved in angiogenesis. Method: In order to identify genes involved in angiogenesis, bovine endothelial cells were treated by a known angiogenesis inhibitor [1], prolactin 16K, and their gene expression profile was compared to the profile of untreated cells. The genes were then divided into three classes: up-regulated, down-regulated, and unaffected genes. The 3'UTR regions of these genes were then analysed by machine learning techniques. Different approaches were considered. First, we described each gene by a vector of motif counts in their 3'UTR regions and used machine learning techniques to rank the motifs according to their relevance for separating the genes into the different classes. We considered successively motifs corresponding to the seeds of known micro- RNAs and also all possible motifs of a given length. To rank the motifs, we compared ensemble of decision trees and linear support vector machines. Second, we considered an approach called Segment and Combine that was proposed in [2]. Finally, we also carried out an exhaustive search of all motifs of a given length that satisfy some constraints on specificity and coverage with respect to a given gene category. Results: The ability of the different approaches at identifying relevant motifs was first assessed on genes predicted to be the target of some known miRNAs. In this simple setting, most methods were able to identify the micro-RNA seed. The results obtained on the genes regulated by prolactin 16K are also very encouraging. We were able to identify one micro-RNA already known to play a role in angiogenesis and several motifs are predicted by different approaches as very specific of up- or down-regulation by prolactin 16K. Their relationship with known micro-RNAs is certainly worth exploring. Conclusion: Machine learning approaches are promising techniques for the identification of micro-RNA/gene interactions. Future work will concern the application of the same kind of techniques on promoters for the identification of transcription factor binding sites. [less ▲]

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See detailRandom Subwindows and Randomized Trees for Image Retrieval, Classification, and Annotation
Marée, Raphaël ULg; Dumont, Marie; Geurts, Pierre ULg et al

Poster (2007, July 22)

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See detailApplication of the Galileo system for a better synchronization of electrical power systems
Mack, Philippe; Capitanescu, Florin ULg; Glavic, Mevludin et al

(2007, July)

In this paper we present objectives and strategies of the NAVELEC research project funded by the European Union Galileo Joint Undertaking (GJU/05/2423). The project objective is to assess how the European ... [more ▼]

In this paper we present objectives and strategies of the NAVELEC research project funded by the European Union Galileo Joint Undertaking (GJU/05/2423). The project objective is to assess how the European Global Navigation Satellite System related solutions can enhance the operation and control of the European power system over wide areas. Some basic functionalities of the GALILEO system are given first, then we summarize the questionnaire, set at the initial stage of the project, and provide the analysis of received responses from transmission system operators. Two generic strategies to upgrade existing electrical power transmission networks with functionalities, as well as major steps to upgrade underlying infrastructures, are discussed in this paper. [less ▲]

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See detailPREDetector: A new tool to identify regulatory elements in bacterial genomes
Hiard, Samuel ULg; Marée, Raphaël ULg; Colson, Séverine ULg et al

in Biochemical and Biophysical Research Communications (2007), 357(4), 861-864

In the post-genomic area, the prediction of transcription factor regulons by position weight matrix-based programmes is a powerful approach to decipher biological pathways and to modelize regulatory ... [more ▼]

In the post-genomic area, the prediction of transcription factor regulons by position weight matrix-based programmes is a powerful approach to decipher biological pathways and to modelize regulatory networks in bacteria. The main difficulty once a regulon prediction is available is to estimate its reliability prior to start expensive experimental validations and therefore trying to find a way how to identify true positive hits from an endless list of potential target genes of a regulatory protein. Here we introduce PREDetector (Prokaryotic Regulatory Elements Detector), a tool developed for predicting regulons of DNA-binding proteins in bacterial genomes that, beside the automatic prediction, scoring and positioning of potential binding sites and their respective target genes in annotated bacterial genomes, it also provides an easy way to estimate the thresholds where to find reliable possible new target genes. PREDetector can be downloaded freely at http://www.montefiore.ulg.ac.be/-hiard/PreDetector (c) 2007 Published by Elsevier Inc. [less ▲]

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See detailImproving the statement of the corrective security-constrained optimal power flow problem
Capitanescu, Florin ULg; Wehenkel, Louis ULg

in IEEE Transactions on Power Systems (2007), 22(2), 887-889

This letter proposes a formulation of the corrective security-constrained optimal power-flow problem imposing, in addition to the classical post-contingency constraints, existence and viability ... [more ▼]

This letter proposes a formulation of the corrective security-constrained optimal power-flow problem imposing, in addition to the classical post-contingency constraints, existence and viability constraints on the short-term equilibrium reached just after contingency. The rationale for doing so is discussed and supported by two examples. [less ▲]

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See detailInterior-point based algorithms for the solution of optimal power flow problems
Capitanescu, Florin ULg; Glavic, Mevludin; Ernst, Damien ULg et al

in Electric Power Systems Research (2007), 77(5-6), 508-517

Interior-point method (IPM) is a very appealing approach to the optimal power flow (OPF) problem mainly due to its speed of convergence and ease of handling inequality constraints. This paper analyzes the ... [more ▼]

Interior-point method (IPM) is a very appealing approach to the optimal power flow (OPF) problem mainly due to its speed of convergence and ease of handling inequality constraints. This paper analyzes the ability of three interior-point (IP) based algorithms, namely the pure primal-dual (PD), the predictor-corrector (PC) and the multiple centrality corrections (MCC), to solve various classical OPF problems: minimization of overall generation cost, minimization of active power losses, maximization of power system loadability and minimization of the amount of load curtailment. These OPF variants have been formulated using a rectangular model for the (complex) voltages. Numerical results on three test systems of 60, 118 and 300 buses are reported. (C) 2006 Elsevier B.V. All rights reserved. [less ▲]

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See detailProjecting Generation Decisions Induced by a Stochastic Program on a Family of Supply Curve Functions
Defourny, Boris ULg; Wehenkel, Louis ULg

Scientific conference (2007, March)

We propose to post-process the results of a scenario based stochastic program by projecting its decisions on a parameterized space of policies. By doing so the risk of overfitting to the set of scenarios ... [more ▼]

We propose to post-process the results of a scenario based stochastic program by projecting its decisions on a parameterized space of policies. By doing so the risk of overfitting to the set of scenarios used in the stochastic program is reduced. A proper choice of the structure of the space of policies allows one to exploit them in the context of novel scenarios, be it for Monte-Carlo based value estimation or for use in real-life conditions. These ideas are presented in the context of planning the exploitation of electric energy resources or for evaluating the economic value of a portfolio of assets. [less ▲]

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See detailPREDetector : Prokaryotic Regulatory Element Detector
Hiard, Samuel ULg; Rigali, Sébastien ULg; Colson, Séverine ULg et al

Poster (2007, February 15)

Background: In the post-genomic area, in silico predictions of regulatory networks are considered as a powerful approach to decipher and understand biological pathways within prokaryotic cells. The ... [more ▼]

Background: In the post-genomic area, in silico predictions of regulatory networks are considered as a powerful approach to decipher and understand biological pathways within prokaryotic cells. The emergence of position weight matrices based programs has facilitated the access to this approach. However, a tool that automatically estimates the reliability of the predictions and would allow users to extend predictions in genomic regions generally regarded with no regulatory functions was still highly demanded. Result: Here, we introduce PREDetector, a tool developed for predicting regulons of DNA-binding proteins in prokaryotic genomes that (i) automatically predicts, scores and positions potential binding sites and their respective target genes, (ii) includes the downstream co-regulated genes, (iii) extends the predictions to coding sequences and terminator regions, (iv) saves private matrices and allows predictions in other genomes, and (v) provides an easy way to estimate the reliability of the predictions. Conclusion: We present, with PREDetector, an accurate prokaryotic regulon prediction tool that maximally answers biologists’ requests. PREDetector can be downloaded freely at http://www.montefiore.ulg.ac.be/~hiard/predetectorfr.html [less ▲]

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See detailBiomarker discovery for inflammatory bowel disease, using proteomic serum profiling
Meuwis, Marie-Alice ULg; Fillet, Marianne ULg; Geurts, Pierre ULg et al

in Biochemical Pharmacology (2007), 73(9), 1422-1433

Crohn's disease and ulcerative colitis known as inflammatory bowel diseases (IBD) are chronic immuno-inflammatory pathologies of the gastrointestinal tract. These diseases are multifactorial, polygenic ... [more ▼]

Crohn's disease and ulcerative colitis known as inflammatory bowel diseases (IBD) are chronic immuno-inflammatory pathologies of the gastrointestinal tract. These diseases are multifactorial, polygenic and of unknown etiology. Clinical presentation is non-specific and diagnosis is based on clinical, endoscopic, radiological and histological criteria. Novel markers are needed to improve early diagnosis and classification of these pathologies. We performed a study with 120 serum samples collected from patients classified in 4 groups (30 Crohn, 30 ulcerative colitis, 30 inflammatory controls and 30 healthy controls) according to accredited criteria. We compared protein sera profiles obtained with a Surface Enhanced Laser Desorption Ionization-Time of Flight-Mass Spectrometer (SELDI-TOF-MS). Data analysis with univariate process and a multivariate statistical method based on multiple decision trees algorithms allowed us to select some potential biomarkers. Four of them were identified by mass spectrometry and antibody based methods. Multivariate analysis generated models that could classify samples with good sensitivity and specificity (minimum 80%) discriminating groups of patients. This analysis was used as a tool to classify peaks according to differences in level on spectra through the four categories of patients. Four biomarkers showing important diagnostic value were purified, identified (PF4, MRP8, FIBA and Hpalpha2) and two of these: PF4 and Hpalpha2 were detected in sera by classical methods. SELDI-TOF-MS technology and use of the multiple decision trees method led to protein biomarker patterns analysis and allowed the selection of potential individual biomarkers. Their downstream identification may reveal to be helpful for IBD classification and etiology understanding. [less ▲]

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See detailRandom Subwindows and Multiple Output Decision Trees for Generic Image Annotation
Dumont, Marie; Marée, Raphaël ULg; Geurts, Pierre ULg et al

Poster (2007)

Detailed reference viewed: 42 (7 ULg)