Patrick E. Meyer

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• Professor
• Professor (Associate) at University of Liège

The "quadrature of the circle" is the name given to a famous geometrical problem that has been proven impossible to solve. In this paper, we propose a simple algorithm for approximating the quadrature of the circle with a theoretical accuracy above 99.99%. Then we present compelling evidence that a famous Christogram, nearly two millenia old, could...

Un large extrait du texte soumis est disponible ici: https://hdl.handle.net/2268/316941

The question whether Covid-19 vaccination campaigns could have had an immediate negative impact on excess deaths continues to be debated two years later, in particular in the less than 45 years old. When the age-stratified (anonymized) vaccination status of deceased will be publicly available, the debate should come to an end. In the meantime, this...

Bright field microscopes are particularly useful tools for biologists for cell and tissue observation, phenotyping, cell counting, and so on. Direct cell observation provides a wealth of information on cells’ nature and physiological condition. Microscopic analyses are, however, time-consuming and usually not easy to parallelize. We describe the fa...

Sarcoidosis and lymphoma often share common features on 18F-FDG PET/CT, such as intense hypermetabolic lesions of lymph nodes and multiple organs. We aimed at developing and validating radiomics signatures to differentiate sarcoidosis from Hodgkin (HL) and diffuse large B-cell (DLBCL) lymphoma. Methods: We retrospectively collected 420 patients (16...

The question whether COVID-19 vaccines have no effect on all-cause mortality or perform as intended, that is mainly reduce excess mortality, has been debated recently in the scientific literature. By crossing the all-cause mortality data with the vaccine data from public European databases, we compare the impact on mortality of two variables of int...

Purpose To test the performances of native and tumour to liver ratio (TLR) radiomic features extracted from pre-treatment 2-[¹⁸F] fluoro-2-deoxy-D-glucose ([¹⁸F]FDG) PET/CT and combined with machine learning (ML) for predicting cancer recurrence in patients with locally advanced cervical cancer (LACC). Methods One hundred fifty-eight patients with...

A correction to this paper has been published: https://doi.org10.1007/s00259-021-05397-x

Background Features reproducibility and the generalizability of the models are currently among the most important limitations when integrating radiomics into the clinics. Radiomic features are sensitive to imaging acquisition protocols, reconstruction algorithms and parameters, as well as by the different steps of the usual radiomics workflow. We p...

Many routines in biological experiments require the precise handling of liquid volumes in the range of microliters up to liters. In this paper, we describe a new wireless controller that is adapted to liquid manipulation tasks, in particular when combined with the proposed 3D-printed pumps. It can be built from widely available electronic component...

Keeping an algal culture at a constant turbidity requires expensive and complex devices. We designed a low-cost, user friendly but also highly configurable phototurbidostat using 3D-printing, open-source software and electronics. The device is able to monitor in real time a culture in photobioreactor, and dynamically adjust the conditions to mainta...

Inferring gene regulatory networks from expression data is a very challenging problem that has raised the interest of the scientific community. Different algorithms have been proposed to try to solve this issue, but it has been shown that different methods have some particular biases and strengths, and none of them is the best across all types of d...

Background: Genetic analyses of plant root system development require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are hig...

Biomedical scientific literature is an unexploited treasure. Due to the staggering number of publications it is literally intractable to gather manually all information. Automatized information extraction (IE) is therefore key. An important subtask is the recognition of names in the text as specific entities (named entity recognition, NER). NER for...

Background Genetic analyses of plant root system development require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are high...

Reverse engineering of gene regulatory networks (GRNs) from gene expression data is a classical challenge in systems biology. Thanks to high-throughput technologies, a massive amount of gene-expression data has been accumulated in the public repositories. Modelling GRNs from multiple experiments (also called integrative analysis) has; therefore, na...

This paper presents a novel method for the reconstruction of a neural network connectivity using calcium fluorescence data. We introduce a fast unsupervised method to integrate different networks that reconstructs structural connectivity from neuron activity. Our method improves the state-of-the-art reconstruction method General Transfer Entropy (G...

Root system analysis is a complex task, often performed with fully automated image analysis pipelines. However, the outcome is rarely verified by ground-truth data, which might lead to underestimated biases. We have used a root model, ArchiSimple, to create a large and diverse library of ground-truth root system images (10,000). For each image, thr...

Distribution of the descriptors of the modeled root images.

Definitions of the different descriptors extracted by RIA-J.

Distribution of the properties of the modeled root images.

Root system analysis is a complex task, often performed with fully automated image analysis pipelines. However, the outcome is rarely verified by ground-truth data, which might lead to underestimated biases. We have used a root model, ArchiSimple, to create a large and diverse library of ground-truth root system images (10,000). For each image, thr...

Reverse engineering of gene regulatory networks (GRNs) from gene expression data is a classical challenge in systems biology. Thanks to high-throughput technologies, a massive amount of gene-expression data has been accumulated in the public repositories. Modelling GRNs from multiple experiments (also called integrative analysis) has; therefore, na...

Reverse engineering of gene regulatory networks (GRNs) from gene expression data is a classical challenge in systems biology. Thanks to high-throughput technologies, a massive amount of gene-expression data has been accumulated in the public repositories. Modelling GRNs from multiple experiments (also called integrative analysis) has; therefore, na...

Background In the last decade, a great number of methods for reconstructing gene regulatory networks from expression data have been proposed. However, very few tools and datasets allow to evaluate accurately and reproducibly those methods. Hence, we propose here a new tool, able to perform a systematic, yet fully reproducible, evaluation of transcr...

This paper presents a novel method for the reconstruction of a neural network connectivity using calcium fluorescence data. We introduce a fast unsupervised method to integrate different networks that reconstructs structural connectivity from neuron activity. Our method improves the state-of-the-art reconstruction method General Transfer Entropy (G...

This chapter introduces the curse of dimensionality, and focuses on widely used variable exploration strategies. The chapter also introduces the information-theoretic framework and recalls variable selection techniques which have been proposed in the literature. It discusses estimation techniques that can be used for implementing the selection stra...

To gain insight into how genomic information is translated into cellular and developmental programs, the Drosophila model organism Encyclopedia of DNA Elements (modENCODE) project is comprehensively mapping transcripts, histone modifications, chromosomal proteins, transcription factors, replication proteins and intermediates, and nucleosome propert...

Improving the detection of relevant variables using a new bivariate measure could importantly impact variable selection and large network inference methods. In this paper, we propose a new statistical coefficient that we call the rank minrelation coefficient. We define a minrelation of X to Y (or equivalently a majrelation of Y to X) as a measure t...

Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the ne...

Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the ne...

To gain insight into how genomic information is translated into cellular and developmental programs, the Drosophila model organism Encyclopedia of DNA Elements (modENCODE) project is comprehensively mapping transcripts, histone modifications, chromosomal proteins, transcription factors, replication proteins and intermediates, and nucleosome propert...

To gain insight into how genomic information is translated into cellular and developmental programs, the Drosophila model organism Encyclopedia of DNA Elements (modENCODE) project is comprehensively mapping transcripts, histone modifications, chromosomal proteins, transcription factors, replication proteins and intermediates, and nucleosome propert...

The importance of bringing causality into play when designing feature selection meth- ods is more and more acknowledged in the machine learning community. This paper proposes a filter approach based on infor- mation theory which aims to prioritise di- rect causal relationships in feature selection problems where the ratio between the num- ber of fe...

— Unraveling transcriptional regulatory networks is essential for understanding and predicting cellular responses in different developmental and environmental contexts. Information-theoretic methods of network inference have been shown to produce high-quality reconstructions because of their ability to infer both linear and non-linear dependencies...

The reverse engineering of transcription regulatory networks from expression data is gaining large interest in the bioinformatics community. An important family of inference techniques is represented by algorithms based on information theoretic measures which rely on the computation of pairwise mutual information. This paper aims to study the impac...

Results This paper presents the R/Bioconductor package minet (version 1.1.6) which provides a set of functions to infer mutual information networks from a dataset. Once fed with a microarray dataset, the package returns a network where nodes denote genes, edges model statistical dependencies between genes and the weight of an edge quantifies the st...

The paper presents an original model-based approach for feature selection and its application to classification of microarray datasets. Model-based approaches to feature selection are generally denoted as wrappers. Wrapper methods assess subsets of variables according to their usefulness to a given prediction model which will be eventually used for...

The paper presents an original filter approach for effective feature selection in microarray data characterized by a large number of input variables and a few samples. The approach is based on the use of a new information-theoretic selection, the double input symmetrical relevance (DISR), which relies on a measure of variable complementarity. This...

The paper presents MRNET, an original method for inferring genetic networks from microarray data. The method is based on maximum relevance/minimum redundancy (MRMR), an effective information-theoretic technique for feature selection in supervised learning. The MRMR principle consists in selecting among the least redundant variables the ones that ha...

The paper presents MRNet, an original method for inferring genetic networks from microarray data. This method is based on Maximum Relevance - Minimum Redundancy (MRMR), an effective information-theoretic technique for feature selection. MRNet is compared experimentally to Relevance Networks (RelNet) and ARACNE, two state-of-the-art information-theo...

The paper presents MRNet, an original method for inferring genetic networks from microarray data. This method is based on Maximum Relevance – Minimum Redundancy (MRMR), an effective information-theoretic technique for feature selection. MRNet is compared experimentally to Relevance Networks (RelNet) and ARACNE, two state-of-the-art information-theo...

The paper presents an original filter approach for eective feature selection in classification tasks with a very large number of input variables. The approach is based on the use of a new information theo- retic selection criterion: the double input symmetrical relevance (DISR). The rationale of the criterion is that a set of variables can return a...

The paper proposes a technique for speeding up the search of the optimal set of features in classification problems where the input variables are discrete or nominal. The approach is based on the definition of an upper bound on the mutual information between the target and a set of d input variables. This bound is derived as a function of the mutua...

This paper presents a wrapper method for feature selection that combines Lazy Learning, racing and subsampling techniques. Lazy Learning (LL) is a local learning technique that, once a query is received, extracts a prediction by locally interpolating the neighboring examples of the query which are considered relevant according to a distance measure...

The paper presents an experiment of a collective pulling of one object by three Lego R robots. The robots use two parameters to orient their pulling force: 1. the direction to the nest 2. the local perception of the movement of the prey. Three behavior-based programs have been experimented to study the impact of each param-eter. Although a solution...

remifentanil. In Eurosiva 2006 Meeting, Madrid, Spain. abstract. Bontempi, G. (2006). A blocking strategy to improve gene selection for clas-