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

in JMLR: Workshop and Conference Proceedings (2008), 4

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See detailMonomeric calgranulins measured by SELDI-TOF mass spectrometry and calprotectin measured by ELISA as biomarkers in arthritis
De Seny, Dominique ULg; Fillet, Marianne ULg; Ribbens, Clio ULg et al

in Clinical Chemistry (2008), 54

BACKGROUND: SELDI-TOF mass spectrometry (MS) is a high-throughput proteomic approach with potential for identifying novel forms of serum biomarkers of arthritis. METHODS: We used SELDI-TOF MS to analyze ... [more ▼]

BACKGROUND: SELDI-TOF mass spectrometry (MS) is a high-throughput proteomic approach with potential for identifying novel forms of serum biomarkers of arthritis. METHODS: We used SELDI-TOF MS to analyze serum samples from patients with various forms of inflammatory arthritis. Several protein profiles were collected on different Bio-Rad Laboratories ProteinChip arrays (CM10 and IMAC-Cu(2+)) and were evaluated statistically to select potential biomarkers. RESULTS: SELDI-TOF MS analyses identified several calgranulin proteins [S100A8 (calgranulin A), S100A9 (calgranulin B), S100A9*, and S100A12 (calgranulin C)], serum amyloid A (SAA), SAA des-Arg (SAA-R), and SAA des-Arg/des-Ser (SAA-RS) as biomarkers and confirmed the results with other techniques, such as western blotting, immunoprecipitation, and nano-LC-MS/MS. The S100 proteins were all able to significantly differentiate samples from patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA), and ankylosing spondylitis (AS) from those of patients with inflammatory bowel diseases used as an inflammatory control (IC) group, whereas the SAA, SAA-R, and SAA-RS proteins were not, with the exception of AS. The 4 S100 proteins were coproduced in all of the pathologies and were significantly correlated with the plasma calprotectin concentration; however, these S100 proteins were correlated with the SAA peak intensities only in the RA and IC patient groups. In RA, these S100 proteins (except for S100A12) were significantly correlated with the serum concentrations of C-reactive protein, matrix metalloproteinase 3, and anti-cyclic citrullinated peptide and with the Disease Activity Score (DAS(28)). CONCLUSIONS: The SELDI-TOF MS technology is a powerful approach for analyzing the status of monomeric, truncated, or posttranslationally modified forms of arthritis biomarkers, such as the S100A8, S100A9, S100A12, and SAA proteins. The fact that the SELDI-TOF MS data were correlated with results obtained with the classic calprotectin ELISA test supports the reliability of this new proteomic technique. [less ▲]

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See detailRisk-aware decision making and dynamic programming
Defourny, Boris ULg; Ernst, Damien ULg; Wehenkel, Louis ULg

Conference (2008)

This paper considers sequential decision making problems under uncertainty, the tradeoff between the expected return and the risk of high loss, and methods that use dynamic programming to find optimal ... [more ▼]

This paper considers sequential decision making problems under uncertainty, the tradeoff between the expected return and the risk of high loss, and methods that use dynamic programming to find optimal policies. It is argued that using Bellman's Principle determines how risk considerations on the return can be incorporated. The discussion centers around returns generated by Markov Decision Processes and conclusions concern a large class of methods in Reinforcement Learning. [less ▲]

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See detailVariable selection for dynamic treatment regimes: a reinforcement learning approach
Fonteneau, Raphaël ULg; Wehenkel, Louis ULg; Ernst, Damien ULg

Conference (2008)

Dynamic treatment regimes (DTRs) can be inferred from data collected through some randomized clinical trials by using reinforcement learning algorithms. During these clinical trials, a large set of ... [more ▼]

Dynamic treatment regimes (DTRs) can be inferred from data collected through some randomized clinical trials by using reinforcement learning algorithms. During these clinical trials, a large set of clinical indicators are usually monitored. However, it is often more convenient for clinicians to have DTRs which are only defined on a small set of indicators rather than on the original full set. To address this problem, we analyse the approximation architecture of the state-action value functions computed by the fitted Q iteration algorithm - a RL algorithm - using tree-based regressors in order to identify a small subset of relevant ones. The RL algorithm is then rerun by considering only as state variables these most relevant indicators to have DTRs defined on a small set of indicators. The approach is validated on benchmark problems inspired from the classical ‘car on the hill’ problem and the results obtained are positive. [less ▲]

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See detailCross-entropy based rare-event simulation for the identification of dangerous events in power systems
Belmudes, Florence ULg; Ernst, Damien ULg; Wehenkel, Louis ULg

in Proceedings of the 10th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS-08) (2008)

We propose in this paper a novel approach for identifying rare events that may endanger power system integrity. This approach is inspired by the rare-event simulation literature and, in particular, by the ... [more ▼]

We propose in this paper a novel approach for identifying rare events that may endanger power system integrity. This approach is inspired by the rare-event simulation literature and, in particular, by the cross-entropy (CE) method for rare event simulation. We propose a general framework for exploiting the CE method in the context of power system reliability evaluation, when a severity function defined on the set of possible events is available. The approach is illustrated on the IEEE 30 bus test system when instability mechanisms related to static voltage security are considered. [less ▲]

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See detailAnalyzing transient instability phenomena beyond the classical stability boundary
Ali, Mahmoud; Glavic, Mevludin; Buisson, Jean et al

in Proceedings of the 40th North American Power Symposium (NAPS 2008) (2008)

We consider power systems for which the amount of power produced by their individual power plants is small with respect to the total generation of the system, and analyze how the transient instability ... [more ▼]

We consider power systems for which the amount of power produced by their individual power plants is small with respect to the total generation of the system, and analyze how the transient instability mechanisms of these systems change qualitatively when their size or the dispersion of their generators increases. Simulation results show that loss of synchronism will propagate more slowly and even stop propagating. Given the evolution of power systems towards more dispersed generation and geographically larger interconnections, we conclude that research in transient stability should focus more on the propagation of the loss of synchronism over longer time periods, so as to assess what happens to the overall system subsequently to the loss of synchronism of the first generators. We also argue that such studies might be very useful in order to provide guidelines for setting up power system control schemes to contain the propagation of instabilities, and we discuss some ideas for designing islanding based emergency control schemes for this. [less ▲]

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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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