Letteratura scientifica selezionata sul tema "Modèles de langage de protéines"
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Articoli di riviste sul tema "Modèles de langage de protéines"
Delahaye, Jean-Paul. "Derrière les modèles massifs de langage". Pour la Science N° 555 – janvier, n. 1 (22 dicembre 2023): 80–85. http://dx.doi.org/10.3917/pls.555.0080.
Testo completoHibert, M. F., J. Hoflack, S. Trumppkallmeyer e A. Bruinvels. "Modèles tridimensionnels des récepteurs couplés aux protéines G". médecine/sciences 9, n. 1 (1993): 31. http://dx.doi.org/10.4267/10608/2784.
Testo completoBodinier, M., M. Leroy e K. Adel-Patient. "Modèles animaux d’allergie alimentaire. Application aux protéines de blé". Revue Française d'Allergologie et d'Immunologie Clinique 48, n. 8 (dicembre 2008): 526–32. http://dx.doi.org/10.1016/j.allerg.2008.09.001.
Testo completoDeschamps, Christophe. "Utiliser les grands modèles de langage au quotidien". Archimag 77, Hors série (24 settembre 2024): 29–33. http://dx.doi.org/10.3917/arma.hs77.0029.
Testo completoGayral, Françoise, Daniel Kayser e François Levy. "Logique et sémantique du langage naturel : modèles et interprétation". Intellectica. Revue de l'Association pour la Recherche Cognitive 23, n. 2 (1996): 303–25. http://dx.doi.org/10.3406/intel.1996.1539.
Testo completoFranke, William. "Psychoanalysis as a Hermeneutics of the Subject: Freud, Ricoeur, Lacan". Dialogue 37, n. 1 (1998): 65–82. http://dx.doi.org/10.1017/s0012217300047594.
Testo completoFAVERDIN, P., D. M’HAMED, M. RICO-GÓMEZ e R. VERITE. "La nutrition azotée influence l’ingestion chez la vache laitière". INRAE Productions Animales 16, n. 1 (9 febbraio 2003): 27–37. http://dx.doi.org/10.20870/productions-animales.2003.16.1.3642.
Testo completoCalin, Rodolphe. "À la charnière de l’image et du langage". Articles 41, n. 2 (6 novembre 2014): 253–73. http://dx.doi.org/10.7202/1027218ar.
Testo completoGodart-Wendling, Béatrice. "La philosophie du langage : une jungle de Calais pour la linguistique ?" Cahiers du Centre de Linguistique et des Sciences du Langage, n. 53 (4 marzo 2018): 131–46. http://dx.doi.org/10.26034/la.cdclsl.2018.323.
Testo completoDevillers, Laurence. "Le langage non responsable des systèmes d’intelligence artificielle (IA) générative". Champ lacanien N° 28, n. 1 (2 ottobre 2024): 133–38. http://dx.doi.org/10.3917/chla.028.0133.
Testo completoTesi sul tema "Modèles de langage de protéines"
Hladiš, Matej. "Réseaux de neurones en graphes et modèle de langage des protéines pour révéler le code combinatoire de l'olfaction". Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ5024.
Testo completoMammals identify and interpret a myriad of olfactory stimuli using a complex coding mechanism involving interactions between odorant molecules and hundreds of olfactory receptors (ORs). These interactions generate unique combinations of activated receptors, called the combinatorial code, which the human brain interprets as the sensation we call smell. Until now, the vast number of possible receptor-molecule combinations have prevented a large-scale experimental study of this code and its link to odor perception. Therefore, revealing this code is crucial to answering the long-term question of how we perceive our intricate chemical environment. ORs belong to the class A of G protein-coupled receptors (GPCRs) and constitute the largest known multigene family. To systematically study olfactory coding, we develop M2OR, a comprehensive database compiling the last 25 years of OR bioassays. Using this dataset, a tailored deep learning model is designed and trained. It combines the [CLS] token embedding from a protein language model with graph neural networks and multi-head attention. This model predicts the activation of ORs by odorants and reveals the resulting combinatorial code for any odorous molecule. This approach is refined by developing a novel model capable of predicting the activity of an odorant at a specific concentration, subsequently allowing the estimation of the EC50 value for any OR-odorant pair. Finally, the combinatorial codes derived from both models are used to predict the odor perception of molecules. By incorporating inductive biases inspired by olfactory coding theory, a machine learning model based on these codes outperforms the current state-of-the-art in smell prediction. To the best of our knowledge, this is the most comprehensive and successful application of combinatorial coding to odor quality prediction. Overall, this work provides a link between the complex molecule-receptor interactions and human perception
Alain, Pierre. "Contributions à l'évaluation des modèles de langage". Rennes 1, 2007. http://www.theses.fr/2007REN1S003.
Testo completoThis work deals with the evaluation of language models independently of any applicative task. A comparative study between several language models is generally related to the role that a model has into a complete system. Our objective consists in being independant of the applicative system, and thus to provide a true comparison of language models. Perplexity is a widely used criterion as to comparing language models without any task assumptions. However, the main drawback is that perplexity supposes probability distributions and hence cannot compare heterogeneous models. As an evaluation framework, we went back to the definition of the Shannon's game which is based on model prediction performance using rank based statistics. Our methodology is able to predict joint word sequences that are independent of the task or model assumptions. Experiments are carried out on French and English modeling with large vocabularies, and compare different kinds of language models
Delot, Thierry. "Interrogation d'annuaires étendus : modèles, langage et optimisation". Versailles-St Quentin en Yvelines, 2001. http://www.theses.fr/2001VERS0028.
Testo completoOota, Subba Reddy. "Modèles neurocomputationnels de la compréhension du langage : caractérisation des similarités et des différences entre le traitement cérébral du langage et les modèles de langage". Electronic Thesis or Diss., Bordeaux, 2024. http://www.theses.fr/2024BORD0080.
Testo completoThis thesis explores the synergy between artificial intelligence (AI) and cognitive neuroscience to advance language processing capabilities. It builds on the insight that breakthroughs in AI, such as convolutional neural networks and mechanisms like experience replay 1, often draw inspiration from neuroscientific findings. This interconnection is beneficial in language, where a deeper comprehension of uniquely human cognitive abilities, such as processing complex linguistic structures, can pave the way for more sophisticated language processing systems. The emergence of rich naturalistic neuroimaging datasets (e.g., fMRI, MEG) alongside advanced language models opens new pathways for aligning computational language models with human brain activity. However, the challenge lies in discerning which model features best mirror the language comprehension processes in the brain, underscoring the importance of integrating biologically inspired mechanisms into computational models. In response to this challenge, the thesis introduces a data-driven framework bridging the gap between neurolinguistic processing observed in the human brain and the computational mechanisms of natural language processing (NLP) systems. By establishing a direct link between advanced imaging techniques and NLP processes, it conceptualizes brain information processing as a dynamic interplay of three critical components: "what," "where," and "when", offering insights into how the brain interprets language during engagement with naturalistic narratives. This study provides compelling evidence that enhancing the alignment between brain activity and NLP systems offers mutual benefits to the fields of neurolinguistics and NLP. The research showcases how these computational models can emulate the brain’s natural language processing capabilities by harnessing cutting-edge neural network technologies across various modalities—language, vision, and speech. Specifically, the thesis highlights how modern pretrained language models achieve closer brain alignment during narrative comprehension. It investigates the differential processing of language across brain regions, the timing of responses (Hemodynamic Response Function (HRF) delays), and the balance between syntactic and semantic information processing. Further, the exploration of how different linguistic features align with MEG brain responses over time and find that the alignment depends on the amount of past context, indicating that the brain encodes words slightly behind the current one, awaiting more future context. Furthermore, it highlights grounded language acquisition through noisy supervision and offers a biologically plausible architecture for investigating cross-situational learning, providing interpretability, generalizability, and computational efficiency in sequence-based models. Ultimately, this research contributes valuable insights into neurolinguistics, cognitive neuroscience, and NLP
Marcou, Gilles. "Modèles dynamiques aux grandes échelles des protéines". Bordeaux 1, 2003. http://www.theses.fr/2003BOR12653.
Testo completoThe dynamic of proteins is dominated by slow and large movements involving a large number of atoms. These movements are responsible for the transitions between two very different forms of the same protein. Il was shown that those movements could be described by a small number of degrees of liberty. These were well described by a small sub-ensemble of normal modes of the proteins. In this thesis are presented some bids to relealize from a complete molecular modelling model of proteins, a model reduced to those large cale movements. The importance of the fast movements is shown. Those are reintroduced in the model by two means ; first a scaling factor is applied to the parameters of the bonded interactions of the original force field and second, a stochastic teerm called Generalized Langevin term, is introduced. The thesis explains how to obtain the parameters of this new model. A derived approach is also presented, in which the protein is decomposed in several blocks each being described with the model previously developed. At last, somme examples of efficient calculation of the non bonded interactions are presented using the characteristics of the new model
Hemery, Mathieu. "Modèles d'évolution de protéines en environnement variable". Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066665/document.
Testo completoThis thesis studies the influence of an evolutionary fluctuating environment on the functional architecture of proteins.The appearance of restricted groups of amino acids – sectors, with particular functional, evolutional and structural properties has no simple explanation in the classical paradigm of proteins physics. So we choose to study the role of evolutionary history on the construction of this particular architecture and the resulting properties.We have thus constructed a model of functional protein inspired by the elastic network models, that we have evolved in silico while temporarily varying the targeted function with various frequencies. We have shown that these fluctuations induce a form of sparsity close to that observed in proteins and has identified the key parameters of this phenomenon. We finally investigate the link between the temporal statistics of the environment and the appearance of different independent sectors
Chauveau, Dominique. "Étude d'une extension du langage synchrone SIGNAL aux modèles probabilistes : le langage SIGNalea". Rennes 1, 1996. http://www.theses.fr/1996REN10110.
Testo completoFleurey, Franck. "Langage et méthode pour une ingénierie des modèles fiable". Phd thesis, Université Rennes 1, 2006. http://tel.archives-ouvertes.fr/tel-00538288.
Testo completoTaly, Jean-François. "Evaluation de la structure des modèles de protéines par dynamique moléculaire". Paris 7, 2007. http://www.theses.fr/2007PA077138.
Testo completoIn this study we monitor different protein structural properties along molecular dynamics (MD) trajectories to discriminate correct from erroneous models. These models are based on the sequence-structure alignments provided by our fold recognition method, FROST. We define correct models as being built from alignments of sequences with structures similar to their native structures and erroneous models from alignments of sequences with structures unrelated to their native structures. We built a set of models intended to cover the whole spectrum: from a perfect model, i. E. , the native structure, to a very poor model, i. E. , a random alignment of the test sequence with a structure belonging to another structural class, including several intermediate models based on fold recognition alignments. We submitted these models to 11 ns of MD simulations at 3 different temperatures. We monitored along the corresponding trajectories the mean of the Root-Mean-Square deviations (RMSd) with respect to the initial conformation, the RMSd fluctuations, the number of conformation clusters, the evolution of secondary structures, and new statistical potential scores based on atomic interaction surface areas. None of these criteria alone is 100% efficient in discriminating correct from erroneous models. However if we consider these criteria in combination it is straightforward to discriminate thé two types of models. The ability of discriminating correct from erroneous models allows us to improve the specificity and sensitivity of our fold recognition method for a number of ambiguous cases
Lopes, Marcos. "Modèles inductifs de la sémiotique textuelle". Paris 10, 2002. http://www.theses.fr/2002PA100145.
Testo completoLibri sul tema "Modèles de langage de protéines"
Laughton, Stephen. Le Courrier des affaires en anglais: 50 modèles de lettres. Alleur (Belgique): Marabout, 1989.
Cerca il testo completoBarbier, Franck. UML 2 et MDE: Ingénierie des modèles avec études de cas. Paris: Dunod, 2005.
Cerca il testo completoNovák, Vilém. The alternative mathematical model of linguistic semantics and pragmatics. New York: Plenum, 1992.
Cerca il testo completoNovák, Vilém. The alternative mathematical model of linguistic semantics and pragmatics. New York: Plenum Press, 1992.
Cerca il testo completoGrand, Mark. Patterns in Java. New York: John Wiley & Sons, Ltd., 2002.
Cerca il testo completo1963-, Leitner David M., e Straub John Edward, a cura di. Proteins: Energy, heat and signal flow. Boca Raton: CRC Press, 2010.
Cerca il testo completo1963-, Leitner David M., e Straub John Edward, a cura di. Proteins: Energy, heat, and signal flow. Boca Raton: Taylor & Francis, 2010.
Cerca il testo completoChristopher, Gardner, a cura di. Financial modelling in Python. Chichester, West Sussex: John Wiley & Sons, 2009.
Cerca il testo completo1971-, Schmid Alexander, e Wolff Eberhard 1972-, a cura di. Server component patterns: Component infrastructures illustrated with EJB. Hoboken, J.J: J. Wiley, 2002.
Cerca il testo completoBishop, Judith. C# 3.0 design patterns. Beijing: O'Reilly, 2008.
Cerca il testo completoCapitoli di libri sul tema "Modèles de langage de protéines"
Tabet, Emmanuelle. "Un langage «bouleversé comme le cœur»: conversion religieuse et conversion littéraire chez Chateaubriand". In Dynamiques de conversion: modèles et résistances, 151–59. Turnhout: Brepols Publishers, 2012. http://dx.doi.org/10.1484/m.behe-eb.4.00304.
Testo completoMarot, Patrick. "Deux modèles métaphysiques de la théorie littéraire". In Interactions dans les Sciences du Langage. Interactions disciplinaires dans les Études littéraires, 259–69. Београд: Универзитет у Београду, Филолошки факултет, 2019. http://dx.doi.org/10.18485/efa.2019.11.ch19.
Testo completoFERET, Jérôme. "Analyses des motifs accessibles dans les modèles Kappa". In Approches symboliques de la modélisation et de l’analyse des systèmes biologiques, 337–98. ISTE Group, 2022. http://dx.doi.org/10.51926/iste.9029.ch9.
Testo completoBonin, Patrick. "Chapitre 5. Modèles de la production verbale de mots". In Psychologie du langage, 249–305. De Boeck Supérieur, 2013. http://dx.doi.org/10.3917/dbu.bonin.2013.01.0249.
Testo completoVERGARA, ANGIE RIVERA, FRANCE BEAUREGARD e NATHALIE S. TRÉPANIER. "La classe de langage:". In Des modèles de service pour favoriser l'intégration scolaire, 103–30. Presses de l'Université du Québec, 2010. http://dx.doi.org/10.2307/j.ctv18pgrj2.9.
Testo completoVergara, Angie Rivera, France Beauregard e nathalie S. trépanier. "La classe de langage". In Des modèles de service pour favoriser l'intégration scolaire, 103–30. Presses de l'Université du Québec, 2010. http://dx.doi.org/10.1515/9782760525269-007.
Testo completoNespoulous, Jean-Luc. "11. La « mise en mots » ... De la phrase au discours : modèles psycholinguistiques et pathologie du langage". In Langage et aphasie, 251. De Boeck Supérieur, 1993. http://dx.doi.org/10.3917/dbu.eusta.1993.01.0251.
Testo completoFLEURY SOARES, Gustavo, e Induraj PUDHUPATTU RAMAMURTHY. "Comparaison de modèles d’apprentissage automatique et d’apprentissage profond". In Optimisation et apprentissage, 153–71. ISTE Group, 2023. http://dx.doi.org/10.51926/iste.9071.ch6.
Testo completoRadu-Lefebvre, Miruna, e Eric Michaël Laviolette. "Chapitre 12. L’impact des modèles de rôle positifs et négatifs, selon l’activation d’un but de promotion versus prévention". In Psychologie sociale, communication et langage, 217–37. De Boeck Supérieur, 2011. http://dx.doi.org/10.3917/dbu.caste.2011.01.0217.
Testo completoMartin, Serge. "La voix comme sujet-relation : de la transmission des modèles de langue aux relations de voix". In Sens de la langue. Sens du langage, 127–38. Presses Universitaires de Bordeaux, 2011. http://dx.doi.org/10.4000/books.pub.8072.
Testo completoAtti di convegni sul tema "Modèles de langage de protéines"
Vo Quang Costantini, S., S. Petit, A. Nassif, F. Ferre e B. Fournier. "Perspectives thérapeutiques du matrisome gingival dans la cicatrisation pathologique". In 66ème Congrès de la SFCO. Les Ulis, France: EDP Sciences, 2020. http://dx.doi.org/10.1051/sfco/20206602013.
Testo completo