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Auswahl der wissenschaftlichen Literatur zum Thema „Connaissances rares“
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Zeitschriftenartikel zum Thema "Connaissances rares"
Booto Ekionea, Jean-Pierre, Gérard Fillion, Prosper Bernard und Michel Plaisent. „Les technologies de l’information, la gestion des connaissances et un avantage concurrentiel soutenu : une analyse par la théorie des ressources“. Revue de l’Université de Moncton 41, Nr. 1 (05.10.2011): 247–71. http://dx.doi.org/10.7202/1006096ar.
Der volle Inhalt der QuelleDemangeot, Catherine, Kizhekepat Sankaran und Stephen Tagg. „Dynamiques d’activation, de partage et d’accumulation des connaissances au sein des communautés autonomes de consommateurs en ligne, aux niveaux individuel et collectif“. Recherche et Applications en Marketing (French Edition) 34, Nr. 4 (20.06.2019): 52–79. http://dx.doi.org/10.1177/0767370119849532.
Der volle Inhalt der QuelleManus, Jean-Marie. „? ? ? ? Maladies rares : un Institut, mais… les connaissances ?“ Revue Française des Laboratoires 2002, Nr. 344 (Juni 2002): 8. http://dx.doi.org/10.1016/s0338-9898(02)80001-4.
Der volle Inhalt der QuelleFleck, Dieter. „L'élaboration de traités, source de connaissances nouvelles“. Revue Internationale de la Croix-Rouge 79, Nr. 827 (Oktober 1997): 557–61. http://dx.doi.org/10.1017/s0035336100051832.
Der volle Inhalt der QuelleSALLES, MATHILDE. „Que présuppose l'anaphore dite présuppositionnelle? Sur la coréférenciation des expressions nominales complètes“. Journal of French Language Studies 21, Nr. 2 (28.09.2010): 191–208. http://dx.doi.org/10.1017/s0959269510000311.
Der volle Inhalt der QuelleFriconneau, Marguerite, Annie Archer, Jeanne Malaterre, Françoise Salama und Marie-Christine Ouillade. „Le patient-expert“. médecine/sciences 36 (Dezember 2020): 62–64. http://dx.doi.org/10.1051/medsci/2020206.
Der volle Inhalt der QuelleFajfr und Müller. „Besondere Manifestationsformen der Autoimmunthyreopathie“. Praxis 91, Nr. 27 (01.07.2002): 1151–60. http://dx.doi.org/10.1024/0369-8394.91.27.1151.
Der volle Inhalt der QuelleAvenas, Pierre, und Henriette Walter. „Noms d’animaux et difficultés de traduction“. Meta 55, Nr. 4 (22.02.2011): 769–78. http://dx.doi.org/10.7202/045690ar.
Der volle Inhalt der QuelleMatsuoka, Atsuko Karin. „Collecting Qualitative Data through Interviews with Ethnic Older People“. Canadian Journal on Aging / La Revue canadienne du vieillissement 12, Nr. 2 (1993): 216–32. http://dx.doi.org/10.1017/s0714980800007765.
Der volle Inhalt der QuellePettazzoni, Magali, Fanny Zhao, Céline Renoux und David Cheillan. „Le dépistage néonatal de maladies génétiques en France“. Revue de biologie médicale N° 370, Nr. 1 (01.01.2023): 49–59. http://dx.doi.org/10.3917/rbm.370.0049.
Der volle Inhalt der QuelleDissertationen zum Thema "Connaissances rares"
Dhombres, Ferdinand. „Apports de la modélisation ontologique au partage des connaissances en médecine périnatale“. Paris 6, 2013. http://www.theses.fr/2013PA066669.
Der volle Inhalt der QuelleThe Information Communication Technologies offer new opportunities for knowledge sharing among individuals and between machines. Ontologies are computer artifacts to represent the semantics of data, leading to semantic interoperability. This thesis explores the contributions of ontologies as support for perinatal Medicine knowledge sharing (in the field of rare diseases, prenatal diagnosis and fetal dysmorphology). The design of a rare diseases ontology (in collaboration with Orphanet) is detailled. This resource is evaluated in real situations and showed positive results for knowledge base curation, consistency and quality control procedures and automated rare diseases classifications generation. The ontology produced in this work is the first one of its kind in the field of rare diseases. Its current structure and its compliance with the W3C standards (OWL, RDF, SKOS) allows its use over the Web of Data (Linked Open Data). Evolutions of the model will follow for the representation of fetal dysmorphology (Accordys project) and for the representation of patients cohorts (BNDMR/RaDiCo projects)
Plesse, François. „Intégration de Connaissances aux Modèles Neuronaux pour la Détection de Relations Visuelles Rares“. Thesis, Paris Est, 2020. http://www.theses.fr/2020PESC1003.
Der volle Inhalt der QuelleData shared throughout the world has a major impact on the lives of billions of people. It is critical to be able to analyse this data automatically in order to measure and alter its impact. This analysis is tackled by training deep neural networks, which have reached competitive results in many domains. In this work, we focus on the understanding of daily life images, in particular on the interactions between objects and people that are visible in images, which we call visual relations.To complete this task, neural networks are trained in a supervised manner. This involves minimizing an objective function that quantifies how detected relations differ from annotated ones. Performance of these models thus depends on how widely and accurately annotations cover the space of visual relations.However, existing annotations are not sufficient to train neural networks to detect uncommon relations. Thus we integrate knowledge into neural networks during the training phase. To do this, we model semantic relationships between visual relations. This provides a fuzzy set of relations that more accurately represents visible relations. Using the semantic similarities between relations, the model is able to learn to detect uncommon relations from similar and more common ones. However, the improved training does not always translate to improved detections, because the objective function does not capture the whole relation detection process. Thus during the inference phase, we combine knowledge to model predictions in order to predict more relevant relations, aiming to imitate the behaviour of human observers
Chennen, Kirsley. „Maladies rares et "Big Data" : solutions bioinformatiques vers une analyse guidée par les connaissances : applications aux ciliopathies“. Thesis, Strasbourg, 2016. http://www.theses.fr/2016STRAJ076/document.
Der volle Inhalt der QuelleOver the last decade, biomedical research and medical practice have been revolutionized by the post-genomic era and the emergence of Big Data in biology. The field of rare diseases, are characterized by scarcity from the patient to the domain knowledge. Nevertheless, rare diseases represent a real interest as the fundamental knowledge accumulated as well as the developed therapeutic solutions can also benefit to common underlying disorders. This thesis focuses on the development of new bioinformatics solutions, integrating Big Data and Big Data associated approaches to improve the study of rare diseases. In particular, my work resulted in (i) the creation of PubAthena, a tool for the recommendation of relevant literature updates, (ii) the development of a tool for the analysis of exome datasets, VarScrut, which combines multi-level knowledge to improve the resolution rate
Echajari, Loubna. „Apprentissage organisationnel à partir d’expériences rares et complexes : le rôle de la codification des connaissances. Le cas de deux accidents nucléaires“. Thesis, Université Côte d'Azur (ComUE), 2018. http://www.theses.fr/2018AZUR0013.
Der volle Inhalt der QuelleRare experiences, whether they are positive or negative, surprise by their unexpected and brutal occurrence. However, more surprising is organizations’ incapability to draw lessons from such rare experiences. Indeed, these experiences challenge traditional approaches of organizational learning based on replication and incremental improvement. In addition, rare experiences are often complex: they are composed of a large variety of elements that interact in uncertain ways. As a result, rare experiences are characterized by a high level of causal ambiguity that can lead to superstitious learning. In these circumstances, the literature emphasizes the need to implement deliberate learning based on knowledge codification. However, codification is a double-edged sword, which can produce organizational rigidity. Besides, research remains quite silent on how to achieve a "well-performed codification”. This research addresses the following question: how to develop and implement an appropriate codification strategy to facilitate deliberate organizational learning from rare and complex experiences? This research is conducted in the Institute of Radioprotection and Nuclear Safety. It is based on a critical realist case study which aims to study two deliberate learning process implemented within the institute to learn from two serious nuclear accidents: Fukushima Daiichi accident and Three Mile Island accident. Our results identify three key generative mechanisms of the codification process, their activation modes and how they are combined. These mechanisms are activated by both the environmental context and the emergence of dedicated structures to codification. The combination of these mechanisms forms different configurations that support two distinct learning cycles which are essential for learning from a rare and complex experience
Bermejo, Das Neves Carlos. „Probabilistic semantic network approach for the study of genotype-phenotype relations in the context of human genetic diseases“. Electronic Thesis or Diss., Strasbourg, 2020. http://www.theses.fr/2020STRAJ093.
Der volle Inhalt der QuelleThis thesis is about the development of a method for modeling complex systems using knowledge graphs and automated reasoning algorithms. The modeling method was applied to rare diseases to predict their causes from the genetic to the cellular, physiological, and whole organism levels. For the creation of the knowledge graph, two ontologies, GO and HPO, were used. Since there were no databases with relationships between these ontologies, a machine learning method was developed to infer relationships and applied to both GO and HPO ontologies. The thesis is completed by a machine learning method to infer deleterious effects after a genetic variation called INDEL. Altogether, the artificial intelligence work presented in this doctoral thesis assists rare disease researchers in understanding what happens in the human body at various levels of abstraction, from the occurrence of a genetic variation to the development of a rare disease
Deng, Weikun. „Amélioration du diagnostic et du pronostic dans des conditions de données rares et de connaissances limitées par l'apprentissage automatique informé par la physique et auto-supervisé“. Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSEP107.
Der volle Inhalt der QuelleThis thesis addresses the critical challenge of “sparse data and scarce knowledge” in developing a generic Prognostics and Health Management (PHM) model. A comprehensive literature review highlights the efficacy of hybrid models combining physics-based modeling with machine learning, focusing on Physics-Informed Machine Learning (PIML) and Self-Supervised Learning (SSL) for enhanced learning from unlabeled data. Thereby, this thesis contributes to advancing both PIML and SSL theories and their practical applications in PHM.The first contribution is developing a generic architectural and learning strategy solution for PIML. Various informed approaches are analyzed, and the mimetic theory is proposed to design flexible, physically consistent neurons and interlayer connections. This novel approach leads to the development of the Rotor Finite Elements Mimetic Neural Network (RFEMNN), which mimics rotor finite element-based dynamics to adjust weight distribution and data flow within the neural network. RFEMNN effectively localizes and recognizes compound faults across multiple rotor structures and conditions. To enhance RFEMNN's few-shot diagnostic capability, constraint projection theory and a reinforcement learning strategy are proposed, aligning the learning process with physics. A generic PIML architecture with parallel, independent PI and data-driven branches is proposed, involving a three-stage training process: pre-training the data-driven branch, freezing it to train the PI branch, and joint training of both branches. This method combines optimized local branches into a comprehensive global model, ensuring the PIML model's performance exceeds original data-driven models under spare data context. Moreover, the solid electrolyte interphase growth-informed Dilated CNN model using this approach showcases its superiority, surpassing leading models in predicting lithium-ion battery RUL with small-cycle data.The second contribution is developing an innovative SSL strategy for unlabeled data learning, introducing a Siamese CNN-LSTM model with a custom contrastive loss function. This model extracts robust feature representations by maximizing differences in the same samples presented in varied sequential orders. Variants of downstream tasks are proposed as intermediate objectives in SSL pretext learning, integrating downstream structures into the pre-training model to align representations with downstream requirements. Under this strategy, the proposed Siamese CNN-LSTM excels at predicting RUL on PRONOSTIA-bearing dataset and remains stable even as training data sparsity increases.The final contribution extends PIML concepts for active knowledge discovery on unlabeled data and integrates SSL into the second phase of PIML's three-step training, utilizing both labeled and unlabeled data. A novel Liquid PI structure and an end-to-end Liquid PI-CNN-Selective state space model (CNN-SSM) are developed. The Liquid PI design introduces gated neurons and liquid interlayer connections that adapt dynamically, acquiring physics knowledge through an optimized search within a predefined operator pool. Demonstrated in torque monitoring of robot manipulators, this approach efficiently discovers knowledge using basic physical operators and dynamic weights from unlabeled data. The Liquid PI CNN-SSM processes variable-length input sequences without signal preprocessing, optimizing resources by requiring only 600 KB to handle 23.9 GB of data. It achieves state-of-the-art performance in mixed prognostic tasks, including bearing degradation, tool wear, battery aging, and CFRP tube fatigue, showcasing the originality and versatility of the proposed approach.Future work will apply PHM-specific scaling laws and train on extensive synthetic and industry datasets to build a cross-modal macro-model. It could integrate diagnostic-prognostic capabilities with infinite sequence length processing, continuing to transform PHM methodologies and solutions
Rai, Ghadi C. „Système de connaissance expert dédié à la recherche translationnelle dans les maladies rares“. Thesis, Aix-Marseille, 2016. http://www.theses.fr/2016AIXM5057/document.
Der volle Inhalt der QuelleAbout 6,000 to 8,000 distinct rare diseases exist today and are estimated to affect 6-8% of the world population. The vast majority of them are genetic and for most of them there is no cure. The genomic revolution has increased the hope of specific treatments based on the gene for many diseases. New technologies have emerged, changing drastically data scale produced in biomedical research. In these conditions, treatment and analysis of data are far from trivial and mere routine, despite spectacular advances in computer technology.This thesis reports the creation of bioinformatics systems, capable of helping researchers and clinicians to identify mutations responsible for certain diseases and to develop new therapies. Thus, the Human Splicing Finder and UMD-Predictor systems predict the effect of a mutation on splicing and protein, respectively. Both bioinformatics systems have been validated through high quality reference datasets, and may help clinicians to properly annotate variations of unknown significance. In addition, this thesis offers two new systems for therapeutic purposes: the Skip-E system identifies optimal candidates AONs for exon skipping therapies, and NR-Analyser, a system that predicts premature termination codons potentially candidates to nonsense readthrough therapies.These different systems are part of a larger project dedicated to translational research. With its predictive and therapeutic aspects, this thesis is part of a research strategy matching with the objectives of the IRDiRC (International Rare Diseases Research Consortium)
Szathmary, Laszlo. „Méthodes symboliques de fouille de données avec la plate-forme Coron“. Phd thesis, Université Henri Poincaré - Nancy I, 2006. http://tel.archives-ouvertes.fr/tel-00336374.
Der volle Inhalt der QuelleLes contributions principales de cette thèse sont : (1) nous avons développé et adapté des algorithmes pour trouver les règles d'association minimales non-redondantes ; (2) nous avons défini une nouvelle base pour les règles d'associations appelée “règles fermées” ; (3) nous avons étudié un champ de l'ECBD important mais relativement peu étudié, à savoir l'extraction des motifs rares et des règles d'association rares ; (4) nous avons regroupé nos algorithmes et une collection d'autres algorithmes ainsi que d'autres opérations auxiliaires d'ECBD dans une boîte à outils logicielle appelée Coron.
Fontaine, Benoît. „La connaissance taxonomique des espèces rares : outil ou handicap pour la conservation de la biodiversité ?“ Paris, Muséum national d'histoire naturelle, 2006. http://www.theses.fr/2006MNHN0028.
Der volle Inhalt der QuelleThe abundance of rare and small species is a characteristic of biodiversity, and these species are the least known. Moreover, ca. 1. 75 million species are known, but the global magnitude of biodiversity is probably over 10 million species, maybe many more. Last but not least, we are experiencing a major extinction crisis. Documenting biodiversity is thus a priority, if only to preserve it. Taxonomists are responsible for this documentation, as these are the ones who discover and describe species, but they suffer from a lack of manpower and infrastructure. Considering these facts (lack of knowledge on biodiversity, extinction crisis, taxonomic impediment), we examine the role taxonomists could play in conservation. The core of taxonomical work is double: discriminating species, and naming them. Discriminating species, before naming, gives data on species richness, rarity and size patterns, and could help the choice of conservation areas. When species are named, assessing endemism and threat status is possible, which also allows to orientate conservation actions. We illustrate these contributions to conservation with papers presenting results of terrestrial mollusc inventories in Gabon and French Polynesia. We then analyze the Fauna Europaea database, compiled by taxonomists, which shows that the indicators usually used to measure the state of biodiversity are missing most species and give a partial image of the situation. This thesis ends with an assessment of the interest and possibility of having a French scientific nomenclature for the molluscs of France, to facilitate conservation of poorly-known threatened species. Only taxonomists can deliver data on specific richness and patterns of endemism for the most numerous and least known species. Their contribution allows to take into account all biodiversity, and not only large vertebrates and flowering plants. In this framework, their role is crucial in conservation biology, together with population biologists, geneticists and ecologists
Di, Martino Jean Claude. „Intégration de connaissances dans des systèmes distribués pour l'extraction de raies spectrales dans des images sonar“. Nancy 1, 1995. http://www.theses.fr/1995NAN10008.
Der volle Inhalt der QuelleBücher zum Thema "Connaissances rares"
Books-World und MyPets. Élever des Poules Sans Connaissances Préalables ! le Manuel Complet du Débutant: Tout Savoir Sur l'élevage de Poules Dans Son Jardin - Poulailler, Nourriture, Entretien, Soins, Races, Oeufs, Etc. Independently Published, 2022.
Den vollen Inhalt der Quelle findenGayot, Eugène Nicolas. Connaissance Générale du Boeuf: Études de Zootechnie Pratique Sur les Races Bovines de la France, de l'algérie, de l'angleterre, de l'allemagne, de la Suisse, de l'autriche, de la Russie et de la Belgique, Avec une Atlas de 83 Figures. Creative Media Partners, LLC, 2023.
Den vollen Inhalt der Quelle findenMoll, Louis. Connaissance Generale du Boeuf, Etudes de Zootechnie Pratique Sur les Races Bovines de la France, de l'algerie, de l'angleterre, de l'allemagne, de la Suisse, de l'autriche, de la Russie et de la Belgique, Avec une Atlas de 83 Figures. Creative Media Partners, LLC, 2018.
Den vollen Inhalt der Quelle findenGayot, Eugène Nicolas. Connaissance Générale du Boeuf: Études de Zootechnie Pratique Sur les Races Bovines de la France, de l'algérie, de l'angleterre, de l'allemagne, de la Suisse, de l'autriche, de la Russie et de la Belgique, Avec une Atlas de 83 Figures. Creative Media Partners, LLC, 2023.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Connaissances rares"
MOREIRA, Manuel Alexis. „Les gaz rares comme traceurs géochimiques de la dynamique du manteau terrestre“. In Structure et dynamique de l’intérieur de la Terre 1, 189–238. ISTE Group, 2024. http://dx.doi.org/10.51926/iste.9172.ch6.
Der volle Inhalt der QuelleBrochot, Sylvie, und Stéphane Cartier. „Maîtriser la démesure, construire la confiance : l’inventivité politique des experts face aux risques naturels“. In Le recours aux experts, 285–300. Presses universitaires de Grenoble, 2013. http://dx.doi.org/10.3917/pug.dumou.2013.01.0285.
Der volle Inhalt der QuelleBonjean, Alain P., Philippe Monneveux und Maria Zaharieva. „Le Déméter 2019“. In Hors collection, 311–20. IRIS éditions, 2019. http://dx.doi.org/10.3917/iris.abis.2019.01.0311.
Der volle Inhalt der QuelleMellet, Margot. „Communication scientifique et science ouverte“. In Communication scientifique et science ouverte, 103–20. De Boeck Supérieur, 2023. http://dx.doi.org/10.3917/dbu.annai.2023.01.0103.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Connaissances rares"
Les forêts françaises face au changement climatique. Académie des sciences, Juni 2023. http://dx.doi.org/10.62686/6.
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