References of "Marée, Raphaël"
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See detailA Machine Learning Approach for Material Detection in Hyperspectral Images
Marée, Raphaël ULiege; Stevens, Benjamin ULiege; Geurts, Pierre ULiege et al

in Proc. 6th IEEE Workshop on Object Tracking and Classification Beyond and in the Visible Spectrum (OTCBVS-CVPR09) (2009)

In this paper we propose a machine learning approach for the detection of gaseous traces in thermal infra red hyperspectral images. It exploits both spectral and spatial information by extracting subcubes ... [more ▼]

In this paper we propose a machine learning approach for the detection of gaseous traces in thermal infra red hyperspectral images. It exploits both spectral and spatial information by extracting subcubes and by using extremely randomized trees with multiple outputs as a classifier. Promising results are shown on a dataset of more than 60 hypercubes. [less ▲]

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See detailP2X1 Ion Channels Promote Neutrophil Chemotaxis through Rho Kinase Activation
Lecut, Christelle ULiege; Frederix, Kim ULiege; Johnson, Daniel M et al

in Journal of Immunology (2009)

This study shows that activation of P2X1 ion channels by ATP promotes neutrophil chemotaxis, a process involving Rho kinase-dependent actomyosin-mediated contraction at the cell rear. These ion channels ... [more ▼]

This study shows that activation of P2X1 ion channels by ATP promotes neutrophil chemotaxis, a process involving Rho kinase-dependent actomyosin-mediated contraction at the cell rear. These ion channels may therefore play a significant role in host defense and inflammation. [less ▲]

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See detailProteomics for prediction and characterization of response to infliximab in Crohn's disease: a pilot study.
Meuwis, Marie-Alice ULiege; Fillet, Marianne ULiege; Lutteri, Laurence ULiege et al

in Clinical Biochemistry (2008), 41(12), 960-7

OBJECTIVES: Infliximab is the first anti-TNFalpha accepted by the Food and Drug Administration for use in inflammatory bowel disease treatment. Few clinical, biological and genetic factors tend to predict ... [more ▼]

OBJECTIVES: Infliximab is the first anti-TNFalpha accepted by the Food and Drug Administration for use in inflammatory bowel disease treatment. Few clinical, biological and genetic factors tend to predict response in Crohn's disease (CD) patient subcategories, none widely predicting response to infliximab. DESIGN AND METHODS: Twenty CD patients showing clinical response or non response to infliximab were used for serum proteomic profiling on Surface Enhanced Lazer Desorption Ionisation-Time of Flight-Mass Spectrometry (SELDI-TOF-MS), each before and after treatment. Univariate and multivariate data analysis were performed for prediction and characterization of response to infliximab. RESULTS: We obtained a model of classification predicting response to treatment and selected relevant potential biomarkers, among which platelet aggregation factor 4 (PF4). We quantified PF4, sCD40L and IL-6 by ELISA for correlation studies. CONCLUSIONS: This first proteomic pilot study on response to infliximab in CD suggests association between platelet metabolism and response to infliximab and requires validation studies on a larger cohort of patients. [less ▲]

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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 ULiege; Fillet, Marianne ULiege; Ribbens, Clio ULiege 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 detailCompositional protein analysis of HDL by SELDI-TOF MS during experimental endotoxemia
Levels, Johannes HM; Marée, Raphaël ULiege; Geurts, Pierre ULiege et al

Poster (2008)

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See detailA novel formulation of inhaled doxycycline reduces allergen-induced inflammation, hyperresponsiveness and remodeling by matrix metalloproteinases and cytokines modulation in a mouse model of asthma
Guéders, Maud ULiege; Bertholet, P.; Perin, Fabienne ULiege et al

in Biochemical Pharmacology (2008), 75(2), 514-26

Background In this study, we assess the effectiveness of inhaled doxycycline, a tetracycline antibiotic displaying matrix metalloproteinases (MMP) inhibitory effects to prevent allergen-induced ... [more ▼]

Background In this study, we assess the effectiveness of inhaled doxycycline, a tetracycline antibiotic displaying matrix metalloproteinases (MMP) inhibitory effects to prevent allergen-induced inflammation, hyperresponsiveness and remodeling. MMPs play key roles in the complex cascade of events leading to asthmatic phenotype. Methods Doxycycline was administered by aerosols by the mean of a novel formulation as a complex with hydroxypropyl-gamma-cyclodextrin (HP-gamma-CD) used as an excipient. BALB/c mice (n = 16–24 in each group) were sensitized and exposed to aerosolized ovalbumin (OVA) from day 21 to 27 (short-term exposure protocol) or 5 days/odd weeks from day 22 to 96 (long-term exposure protocol). Results In the short-term exposure model, inhaled doxycycline decreased allergen-induced eosinophilic inflammation in bronchoalveolar lavage (BAL) and in peribronchial areas, as well as airway hyperresponsiveness. In lung tissue, exposure to doxycycline via inhaled route induced a fourfold increase in IL-10 levels, a twofold decrease in IL-5, IL-13 levels and diminished MMP-related proteolysis and the proportion of activated MMP-9 as compared to placebo. In the long-term exposure model, inhaled doxycycline significantly decreased the extent of glandular hyperplasia, airway wall thickening, smooth muscle hyperplasia and subepithelial collagen deposition which are well recognized features of airway remodeling. Conclusion Doxycycline administered by aerosols decreases the allergen-induced airway inflammation and hyperresponsiveness and inhibits the development of bronchial remodeling in a mouse model of asthma by modulation of cytokines production and MMP activity. [less ▲]

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See detailPREDetector : Prokaryotic Regulatory Element Detector
Hiard, Samuel ULiege; Rigali, Sébastien ULiege; Colson, Séverine ULiege 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 detailRandom Subwindows and Randomized Trees for Image Retrieval, Classification, and Annotation
Marée, Raphaël ULiege; Dumont, Marie; Geurts, Pierre ULiege et al

Poster (2007, July 22)

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See detailPREDetector: A new tool to identify regulatory elements in bacterial genomes
Hiard, Samuel ULiege; Marée, Raphaël ULiege; Colson, Séverine ULiege 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 detailPREDetector : Prokaryotic Regulatory Element Detector
Hiard, Samuel ULiege; Rigali, Sébastien ULiege; Colson, Séverine ULiege 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 detailRandom Subwindows and Multiple Output Decision Trees for Generic Image Annotation
Dumont, Marie; Marée, Raphaël ULiege; Geurts, Pierre ULiege et al

Poster (2007)

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

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

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 detailNouvelles approches dans la prise en charge de l'infection a VIH.
Chandrika, K.; Dellot, Patricia ULiege; Frippiat, Frédéric ULiege 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 ULiege; Rigali, Sébastien ULiege; Colson, Séverine ULiege 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 detailReinforcement learning with raw image pixels as input state
Ernst, Damien ULiege; Marée, Raphaël ULiege; Wehenkel, Louis ULiege

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 detailBiological Image Classification with Random Subwindows and Extra-Trees
Marée, Raphaël ULiege; Geurts, Pierre ULiege; Wehenkel, Louis ULiege

Conference (2006)

We illustrate the potential of our image classification method on three datasets of images at different imaging modalities/scales, from subcellular locations up to human body regions. The method is based ... [more ▼]

We illustrate the potential of our image classification method on three datasets of images at different imaging modalities/scales, from subcellular locations up to human body regions. The method is based on random subwindows extraction and the combination of their classification using ensembles of extremely randomized decision trees. [less ▲]

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See detailSegment and combine: a generic approach for supervised learning of invariant classifiers from topologically structured data
Geurts, Pierre ULiege; Marée, Raphaël ULiege; Wehenkel, Louis ULiege

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

A generic method for supervised classification of structured objects is presented. The approach induces a classifier by (i) deriving a surrogate dataset from a pre-classified dataset of structured objects ... [more ▼]

A generic method for supervised classification of structured objects is presented. The approach induces a classifier by (i) deriving a surrogate dataset from a pre-classified dataset of structured objects, by segmenting them into pieces, (ii) learning a model relating pieces to object-classes, (iii) classifying structured objects by combining predictions made for their pieces. The segmentation allows to exploit local information and can be adapted to inject invariances into the resulting classifier. The framework is illustrated on practical sequence, time-series and image classification problems. [less ▲]

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See detailRandom Subwindows for Robust Image Classification
Marée, Raphaël ULiege; Geurts, Pierre ULiege; Piater, Justus ULiege et al

in Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2005) (2005)

We present a novel, generic image classification method based on a recent machine learning algorithm (ensembles of extremely randomized decision trees). Images are classified using randomly extracted ... [more ▼]

We present a novel, generic image classification method based on a recent machine learning algorithm (ensembles of extremely randomized decision trees). Images are classified using randomly extracted subwindows that are suitably normalized to yield robustness to certain image transformations. Our method is evaluated on four very different, publicly available datasets (COIL-100, ZuBuD, ETH-80, WANG). Our results show that our automatic approach is generic and robust to illumination, scale, and viewpoint changes. An extension of the method is proposed to improve its robustness with respect to rotation changes. [less ▲]

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See detailDecision Trees and Random Subwindows for Object Recognition
Marée, Raphaël ULiege; Geurts, Pierre ULiege; Piater, Justus ULiege et al

in ICML workshop on Machine Learning Techniques for Processing Multimedia Content (MLMM2005) (2005)

In this paper, we compare five tree-based machine learning methods within a recent generic image classification framework based on random extraction and classification of subwindows. We evaluate them on ... [more ▼]

In this paper, we compare five tree-based machine learning methods within a recent generic image classification framework based on random extraction and classification of subwindows. We evaluate them on three publicly available object recognition datasets (COIL-100, ETH-80, and ZuBuD). Our comparison shows that this general and conceptually simple framework yields good results when combined with ensemble of decision trees, especially when using Tree Boosting or Extra-Trees. The latter is also particularly attractive in terms of computational efficiency. [less ▲]

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