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

Poster (2007)

Detailed reference viewed: 77 (8 ULiège)
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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 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 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 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 detailClassification automatique d'images par arbres de d́ecision
Marée, Raphaël ULiege

Doctoral thesis (2005)

The work presented in this thesis is motivated by the problem of automatic image classification. Image classification methods seek to automatically classify previously unseen images using databases of ... [more ▼]

The work presented in this thesis is motivated by the problem of automatic image classification. Image classification methods seek to automatically classify previously unseen images using databases of labeled images provided by human experts. The main contribution of this thesis is a novel approach for image classification that has been shown to perform well on a variety of tasks. It uses some recent machine learning algorithms based on ensembles of decision trees that we applied directly on pixel values. We combine it with techniques of random extraction and transformation of subwindows from images so as to improve robustness to certain image transformations. The method has been evaluated on 7 publicly available datasets corresponding to various image classification tasks: recognition of handwritten digits, faces, 3D objects, textures, buildings, themes, or landscapes. Some of these datasets contain images representing widely varying conditions: occlusions, cluttered background, illumination, viewpoint, orientation, and scale changes. The accuracy of our method is generally comparable with the state of the art and it is particularly attractive in terms of computational efficiency. [less ▲]

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See detailBiomedical image classification with random subwindows and decision trees
Marée, Raphaël ULiege; Geurts, Pierre ULiege; Piater, Justus ULiege et al

in Computer Vision for Biomedical Image Applications (2005)

In this paper, we address a problem of biomedical image classification that involves the automatic classification of x-ray images in 57 predefined classes with large intra-class variability. To achieve ... [more ▼]

In this paper, we address a problem of biomedical image classification that involves the automatic classification of x-ray images in 57 predefined classes with large intra-class variability. To achieve that goal, we apply and slightly adapt a recent generic method for image classification based on ensemble of decision trees and random subwindows. We obtain classification results close to the state of the art on a publicly available database of 10000 x-ray images. We also provide some clues to interpret the classification of each image in terms of subwindow relevance. [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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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 detailA generic approach for image classification based on decision tree ensembles and local sub-windows
Marée, Raphaël ULiege; Geurts, Pierre ULiege; Piater, Justus ULiege et al

in Proceedings of the 6th Asian Conference on Computer Vision (2004)

A novel and generic approach for image classification is presented. The method operates directly on pixel values and does not require feature extraction. It combines a simple local sub-window extraction ... [more ▼]

A novel and generic approach for image classification is presented. The method operates directly on pixel values and does not require feature extraction. It combines a simple local sub-window extraction technique with induction of ensembles of extremely randomized decision trees. We report results on four well known and publicly available datasets corresponding to representative applications of image classification problems: handwritten digits (MNIST), faces (ORL), 3D objects (COIL-100), and textures (OUTEX). A comparison with studies from the computer vision literature shows that our method is competitive with the state of the art, an interesting result considering its generality and conceptual simplicity. Further experiments are carried out on the COIL-100 dataset to evaluate the robustness of the learned models to rotation, scaling, or occlusion of test images. These preliminary results are very encouraging [less ▲]

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See detailUne méthode générique pour la classification automatique d'images à partir des pixels
Marée, Raphaël ULiege; Geurts, Pierre ULiege; Wehenkel, Louis ULiege

in Revue des Nouvelles Technologies de l'Information (2003), 1

Dans cet article, nous évaluons une approche générique de classification automatique d'images. Elle repose sur une méthode d'apprentissage récente qui construit des ensembles d'arbres de décision par ... [more ▼]

Dans cet article, nous évaluons une approche générique de classification automatique d'images. Elle repose sur une méthode d'apprentissage récente qui construit des ensembles d'arbres de décision par sélection aléatoire des tests directement sur les valeurs basiques des pixels. Nous proposons une variante, également générique, qui réalise une augmentation fictive de la taille des échantillons par extraction et classification de sous-fenêtres des images. Ces deux approches sont évaluées et comparées sur quatre bases de données publiques de problèmes courants: la reconnaissance de chiffres manuscrits, de visages, d'objets 3D et de textures. [less ▲]

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See detailAn empirical comparison of machine learning algorithms for generic image classification
Marée, Raphaël ULiege; Geurts, Pierre ULiege; Visimberga, Giorgio et al

in Proceedings of the 23rd SGAI international conference on innovative techniques and applications of artificial intelligence, Research and development in intelligent systems XX, (2003)

Detailed reference viewed: 58 (3 ULiège)