References of "Wehenkel, Louis"
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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)

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See detailRandom subwindows and extremely randomized trees for image classification in cell biology
Marée, Raphaël ULg; Geurts, Pierre ULg; Wehenkel, Louis ULg

in BMC Cell Biology (2007), 8(Suppl. 1),

Background: With the improvements in biosensors and high-throughput image acquisition technologies, life science laboratories are able to perform an increasing number of experiments that involve the ... [more ▼]

Background: With the improvements in biosensors and high-throughput image acquisition technologies, life science laboratories are able to perform an increasing number of experiments that involve the generation of a large amount of images at different imaging modalities/scales. It stresses the need for computer vision methods that automate image classification tasks. Results: We illustrate the potential of our image classification method in cell biology by evaluating it on four datasets of images related to protein distributions or subcellular localizations, and red-blood cell shapes. Accuracy results are quite good without any specific pre-processing neither domain knowledge incorporation. The method is implemented in Java and available upon request for evaluation and research purpose. Conclusion: Our method is directly applicable to any image classification problems. We foresee the use of this automatic approach as a baseline method and first try on various biological image classification problems. [less ▲]

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See detailA collaborative framework for multi-area dynamic security assessment of large scale systems
Wehenkel, Louis ULg; Glavic, Mevludin; Ernst, Damien ULg

in Proceedings of the 2007 Power Tech (2007)

In this paper we propose a collaborative framework to carry out multi-area dynamic security assessment over an interconnection operated by a team of TSOs responsible of different areas. In this framework ... [more ▼]

In this paper we propose a collaborative framework to carry out multi-area dynamic security assessment over an interconnection operated by a team of TSOs responsible of different areas. In this framework each TSO does his part of the work and, thanks to information exchange and coordination rules, potential security problems can be detected by all the involved TSOs. We find that distributed multi-area security assessment is achievable and useful if, on the one hand, each TSO can provide an appropriate dynamic equivalent model of his area and if, on the other hand, he is able to publish stability bounds on his inflows under which the dynamic performance of his system would remain acceptable. We then discuss the notions of dynamic equivalent model and external stability domain characterization of an area and identify techniques for deriving such equivalents and stability bounds within the proposed framework. [less ▲]

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See detailThe cross-entropy method for power system combinatorial optimization problems
Ernst, Damien ULg; Glavic, Mevludin; Stan, Guy-Bart et al

in Proceedings of the 2007 Power Tech (2007)

We present an application of a cross-entropy based combinatorial optimization method for solving some unit commitment problems. We report simulation results and analyze, under several perspectives ... [more ▼]

We present an application of a cross-entropy based combinatorial optimization method for solving some unit commitment problems. We report simulation results and analyze, under several perspectives (accuracy, computing times, ability to solve efficiently large-scale problems), the performances of the approach. [less ▲]

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See detailModel predictive control and reinforcement learning as two complementary frameworks
Ernst, Damien ULg; Glavic, Mevludin; Capitanescu, Florin ULg et al

in International Journal of Tomography & Statistics (2007), 6

Model predictive control (MPC) and reinforcement learning (RL) are two popular families of methods to control system dynamics. In their traditional setting, they formulate the control problem as a ... [more ▼]

Model predictive control (MPC) and reinforcement learning (RL) are two popular families of methods to control system dynamics. In their traditional setting, they formulate the control problem as a discrete-time optimal control problem and compute a suboptimal control policy. We present in this paper in a unified framework these two families of methods. We run for MPC and RL algorithms simulations on a benchmark control problem taken from the power system literature and discuss the results obtained. [less ▲]

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See detailContent-based Image Retrieval by Indexing Random Subwindows with Randomized Trees
Marée, Raphaël ULg; Geurts, Pierre ULg; Wehenkel, Louis ULg

in Proc. 8th Asian Conference on Computer Vision (ACCV), LNCS (2007)

We propose a new method for content-based image retrieval which exploits the similarity measure and indexing structure of totally randomized tree ensembles induced from a set of subwindows randomly ... [more ▼]

We propose a new method for content-based image retrieval which exploits the similarity measure and indexing structure of totally randomized tree ensembles induced from a set of subwindows randomly extracted from a sample of images. We also present the possibility of updating the model as new images come in, and the capability of comparing new images using a model previously constructed from a different set of images. The approach is quantitatively evaluated on various types of images with state-of-the-art results despite its conceptual simplicity and computational efficiency [less ▲]

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See detailE-SIME- A method for transient stability closed-loop emergency control: achievements and prospects
Glavic, Mevludin; Ernst, Damien ULg; Ruiz-Vega, Daniel et al

in Proceedings of 2007 IREP Symposium - Bulk Power Systems Dynamics and Control - VII (2007)

A general response-based technique is presented for closed-loop transient stability emergency control. It relies on E-SIME, derived from the hybrid transient stability method, SIME. E-SIME uses real-time ... [more ▼]

A general response-based technique is presented for closed-loop transient stability emergency control. It relies on E-SIME, derived from the hybrid transient stability method, SIME. E-SIME uses real-time information supposed to be furnished by phasor measurement units to predict the stability status of the power system, and, in view of an imminent instability, to design and trigger appropriate countermeasures, while continuing monitoring in order to check their effectiveness or to apply additional ones. Performance of the method in terms of accuracy and rapidity is scrutinized and illustrated on several real-world power system examples. New technical solutions and algorithms for the accurate estimation and prediction of power system quantities most relevant to the method are discussed. The observations from a recent investigation and conclusions that could prove useful for improving further the method are summarized together with some realistic timing considerations. A natural coupling of the two SIME based emergency control techniques: open-loop emergency control and E-SIME, so as to combine their complementary features is also discussed. [less ▲]

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See detailAutomatic learning for the classification of primary frequency control behaviour
Cornélusse, Bertrand ULg; Wehenkel, Louis ULg; Wera, Claude

in Power Tech, 2007 IEEE Lausanne (2007)

In this paper we propose a methodology based on supervised automatic learning in order to classify the behaviour of generators in terms of their performance in providing primary frequency control ... [more ▼]

In this paper we propose a methodology based on supervised automatic learning in order to classify the behaviour of generators in terms of their performance in providing primary frequency control ancillary services. The problem is posed as a time-series classification problem, and handled by using state-of- the-art supervised learning methods such as ensembles of decision trees and support-vector machines combined with several preprocessing techniques. The method was designed in the context of the Belgian system and is validated on real-life data composed of more than 600 time-series recorded on this system. [less ▲]

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See detailGradient boosting for kernelized output spaces
Geurts, Pierre ULg; Wehenkel, Louis ULg; d'Alché-Buc, Florence

in Proceedings of the 24th International Conference on Machine Learning (2007)

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See detailNouvelles approches dans la prise en charge de l'infection a VIH.
Chandrika, K.; Dellot, Patricia ULg; Frippiat, Frédéric ULg et al

in Revue Médicale de Liège (2007), 62 Spec No

HIV infection remains a major problem of public health in Belgium as well as globally. The number of new diagnosies of HIV infection in Belgium remains between two and three daily. Given the dramatic ... [more ▼]

HIV infection remains a major problem of public health in Belgium as well as globally. The number of new diagnosies of HIV infection in Belgium remains between two and three daily. Given the dramatic effect of antiretroviral therapy on the mortality due to HIV infection, the number of patients is constantly increasing. The different problems related to HIV care are also changing. Aging of the patients and chronic exposure to antiretroviral medications have induced new complications. We will present in this brief article several new experimental and clinical approaches in which our centre has participated during the last two years. [less ▲]

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

Poster (2006, May 17)

PreDetector is a stand-alone software, written in java. Its final aim is to predict regulatory sites for prokaryotic species. It comprises two functionalities. The first one is very similar to Target ... [more ▼]

PreDetector is a stand-alone software, written in java. Its final aim is to predict regulatory sites for prokaryotic species. It comprises two functionalities. The first one is very similar to Target Explorer1. From a set of sequences identified as potential target sites, PreDetector creates a consensus sequence and computes its scoring matrix. This sequence and matrix can be saved on a file and, then, be used to find along a selected genome the sequences that are close enough to the consensus sequence. To this end, a score is attributed to each locus in the genome according to the similarity measure defined by the matrix. The output of this functionality is filtered with a cut-off score and then directly used as input by the second one. The second functionality starts by fetching the gene positions of the selected species from the NCBI server. The loci having above cut-off score are then classified into four classes, allowing multiple classes for one element. This gives the biologists a better view of his discovered sequences. [less ▲]

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See detailExtremely randomized trees
Geurts, Pierre ULg; Ernst, Damien ULg; Wehenkel, Louis ULg

in Machine Learning (2006), 63(1), 3-42

This paper proposes anew tree-based ensemble method for supervised classification and regression problems. It essentially consists of randomizing strongly both attribute and cut-point choice while ... [more ▼]

This paper proposes anew tree-based ensemble method for supervised classification and regression problems. It essentially consists of randomizing strongly both attribute and cut-point choice while splitting a tree node. In the extreme case, it builds totally randomized trees whose structures are independent of the output values of the learning sample. The strength of the randomization can be tuned to problem specifics by the appropriate choice of a parameter. We evaluate the robustness of the default choice of this parameter, and we also provide insight on how to adjust it in particular situations. Besides accuracy, the main strength of the resulting algorithm is computational efficiency. A bias/variance analysis of the Extra-Trees algorithm is also provided as well as a geometrical and a kernel characterization of the models induced. [less ▲]

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See detailModel predictive control and reinforcement learning as two complementary frameworks
Ernst, Damien ULg; Glavic, Mevludin; Capitanescu, Florin ULg et al

in Proceedings of the 13th IFAC Workshop on Control Applications of Optimisation (2006)

Model predictive control (MPC) and reinforcement learning (RL) are two popular families of methods to control system dynamics. In their traditional setting, they formulate the control problem as a ... [more ▼]

Model predictive control (MPC) and reinforcement learning (RL) are two popular families of methods to control system dynamics. In their traditional setting, they formulate the control problem as a discrete-time optimal control problem and compute a suboptimal control policy. We present in this paper in a unified framework these two families of methods. We run for MPC and RL algorithms simulations on a benchmark control problem taken from the power system literature and discuss the results obtained. [less ▲]

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See detailReinforcement learning with raw image pixels as input state
Ernst, Damien ULg; Marée, Raphaël ULg; Wehenkel, Louis ULg

in Advances in machine vision, image processing & pattern analysis (Lecture notes in computer science, Vol. 4153) (2006)

We report in this paper some positive simulation results obtained when image pixels are directly used as input state of a reinforcement learning algorithm. The reinforcement learning algorithm chosen to ... [more ▼]

We report in this paper some positive simulation results obtained when image pixels are directly used as input state of a reinforcement learning algorithm. The reinforcement learning algorithm chosen to carry out the simulation is a batch-mode algorithm known as fitted Q iteration. [less ▲]

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See detailApplications of security-constrained optimal power flows
Capitanescu, Florin ULg; Glavic, Mevludin; Ernst, Damien ULg et al

in In Proceedings of Modern Electric Power Systems Symposium, MEPS06 (2006)

This paper proposes to formulate security control as a sequential decision making problem and presents new developments in automatic learning of sequential decision making strategies from simulations and ... [more ▼]

This paper proposes to formulate security control as a sequential decision making problem and presents new developments in automatic learning of sequential decision making strategies from simulations and/or information collected from real-life system measurements. The exploitation of these methods for the design of decision making strategies and control policies in the context of preventive and emergency mode dynamic security assessment and control is discussed and further opportunities for research in this area are highlighted. [less ▲]

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See detailClinical data based optimal STI strategies for HIV: a reinforcement learning approach
Ernst, Damien ULg; Stan, Guy-Bart; Gonçalves, Jorge et al

in Proceedings of the 45th IEEE Conference on Decision and Control (CDC 2006) (2006)

This paper addresses the problem of computing optimal structured treatment interruption strategies for HIV infected patients. We show that reinforcement learning may be useful to extract such strategies ... [more ▼]

This paper addresses the problem of computing optimal structured treatment interruption strategies for HIV infected patients. We show that reinforcement learning may be useful to extract such strategies directly from clinical data, without the need of an accurate mathematical model of HIV infection dynamics. To support our claims, we report simulation results obtained by running a recently proposed batch-mode reinforcement learning algorithm, known as fitted Q iteration, on numerically generated data. [less ▲]

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See detailClinical data based optimal STI strategies for HIV: a reinforcement learning approach
Ernst, Damien ULg; Stan, Guy-Bart; Gonçalves, Jorge et al

in Proceedings of the 15th Annual Machine Learning Conference of Belgium and The Netherlands (Benelearn 2006) (2006)

This paper addresses the problem of computing optimal structured treatment interruption strategies for HIV infected patients. We show that reinforcement learning may be useful to extract such strategies ... [more ▼]

This paper addresses the problem of computing optimal structured treatment interruption strategies for HIV infected patients. We show that reinforcement learning may be useful to extract such strategies directly from clinical data, without the need of an accurate mathematical model of HIV infection dynamics. To support our claims, we report simulation results obtained by running a recently proposed batch-mode reinforcement learning algorithm, known as tted Q iteration, on numerically generated data. [less ▲]

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See detailMulti-area security assessment: results using efficient bounding method
Wehenkel, Louis ULg; Glavic, Mevludin; Ernst, Damien ULg

in Proceedings of the 38th North American Power Symposium (NAPS 2006) (2006)

We present our recent results on using previously introduced framework for multi-area security assessment in large interconnections. The basic idea of the framework is exchanging just enough information ... [more ▼]

We present our recent results on using previously introduced framework for multi-area security assessment in large interconnections. The basic idea of the framework is exchanging just enough information so that each operator can evaluate the impact in his control area of contingencies both internal and external to his area. We provide illustrations based on a localization concept known as efficient bounding method and recently introduced approximate DC model of the European interconnected system. In this paper we focus on four transmission system operators (within the approximate DC model): Belgium, France, Germany, and Netherlands (for both Summer and Winter-peak load conditions). [less ▲]

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