Letteratura scientifica selezionata sul tema "Algorithmes de prédiction"
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Articoli di riviste sul tema "Algorithmes de prédiction":
Lazaro, Christophe. "Le pouvoir « divinatoire » des algorithmes". Anthropologie et Sociétés 42, n. 2-3 (5 ottobre 2018): 127–50. http://dx.doi.org/10.7202/1052640ar.
HARINAIVO, A., H. HAUDUC e I. TAKACS. "Anticiper l’impact de la météo sur l’influent des stations d’épuration grâce à l’intelligence artificielle". Techniques Sciences Méthodes 3 (20 marzo 2023): 33–42. http://dx.doi.org/10.36904/202303033.
Beaudouin, Valérie, e Winston Maxwell. "La prédiction du risque en justice pénale aux états-unis : l’affaire propublica-compas". Réseaux N° 240, n. 4 (21 settembre 2023): 71–109. http://dx.doi.org/10.3917/res.240.0071.
Saccocio, Christèle, Guillaume Dumain e Olivier Langeron. "Ventilation au masque et intubation difficiles chez l’adulte : de la prédiction à la décision grâce aux algorithmes". Oxymag 34, n. 177 (marzo 2021): 8–15. http://dx.doi.org/10.1016/j.oxy.2021.03.003.
Bourkhime, H., N. Qarmiche, N. Bahra, M. Omari, M. Berraho, N. Tachfouti, S. El Fakir e N. Otmani. "P36 - La prédiction de la dépression chez les Marocains atteints de maladies respiratoires chroniques - Analyse comparative des algorithmes d'apprentissage automatique". Journal of Epidemiology and Population Health 72 (maggio 2024): 202476. http://dx.doi.org/10.1016/j.jeph.2024.202476.
De Oliveira, H., M. Prodel e A. Vainchtock. "Prédiction du coût hospitalier annuel pour des patients vivant avec le VIH : comparaison de 10 algorithmes de « Machine Learning » sur une cohorte identifiée dans les données PMSI". Revue d'Épidémiologie et de Santé Publique 66 (marzo 2018): S27. http://dx.doi.org/10.1016/j.respe.2018.01.057.
K, Ravikiran, P. Gopala Krishna, N. Rajashekhar, K. Sandeep, Y. Saeed Hazim, Uma Reddy, Rajeev Sobti e Ashwani Kumar. "Short-term rainfall prédiction using prédictive analytics: A case study in Telangana". E3S Web of Conferences 507 (2024): 01072. http://dx.doi.org/10.1051/e3sconf/202450701072.
Cournoyer, A., V. Langlois-Carbonneau, R. Daoust e J. Chauny. "LO29: Création dune règle de décision clinique pour le diagnostic dun syndrome aortique aigu avec les outils dintelligence artificielle : phase initiale de définition des attributs communs aux patients sans syndrome aortique aigu chez une population à risque". CJEM 20, S1 (maggio 2018): S16—S17. http://dx.doi.org/10.1017/cem.2018.91.
Im, Virginie, e Michel Briex. "Médecine prédictive, deep learning, algorithmes et accouchement". Spirale N°93, n. 1 (2020): 204. http://dx.doi.org/10.3917/spi.093.0204.
Phillips, Susan P., Sheryl Spithoff e Amber Simpson. "L’intelligence artificielle et les algorithmes prédictifs en médecine". Canadian Family Physician 68, n. 8 (agosto 2022): e230-e233. http://dx.doi.org/10.46747/cfp.6808e230.
Tesi sul tema "Algorithmes de prédiction":
Vekemans, Denis. "Algorithmes pour méthodes de prédiction". Lille 1, 1995. http://www.theses.fr/1995LIL10176.
Engelen, Stéfan. "Algorithmes pour la prédiction de structures secondaires d'ARN". Evry-Val d'Essonne, 2006. http://www.theses.fr/2006EVRY0008.
The knowledge of RNA secondary structure is important to understand the relation between structure and function of the RNA. It is made up of a set of helices resulting from the folding of succession of a complementary base pairs. Complexities of existing algorithms is at least of O(n3). This thesis presents an algorithm, called P-DCFold, based on the comparative approach, for the prediction of RNA secondary structures with a complexity of O(n2). In this algorithm, helices are searched recursively using the "divide and conquer" approach. The selection of helices is based on thermodynamic and covariation criteria. The main problem of the comparative approach is the low quality of used alignment. So, P-DCfold use evolutionary models under structure constraints to select correctly aligned sequences. P-DCFold predicts the secondary structure of several RNA with a sensitivity of 0,85 and a sensibility of 0,95
Becquey, Louis. "Algorithmes multi-critères pour la prédiction de structures d'ARN". Electronic Thesis or Diss., université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG065.
Computational RNA structure prediction methods rely on two major algorithmic steps : a sampling step, to propose new structure solutions, and a scoring step to sort the solutions by relevance. A wide diversity of scoring methods exists. Some rely on physical models, some on the similarity to already observed data (so-called data based methods, or knowledge based methods). This thesis proposes structure prediction methods combining two or more scoring criterions, diverse regarding the modelling scale (secondary structure, tertiary structure), their type (theory-based, knowledge-based, compatibility with experimental chemical probing results). The methods describe the Pareto front of the multi-objective optimization problem formed by these criteria. This allows to identify solutions (structures) well scored on each criterion, and to study the correlation between criterions. The presented approaches exploit the latest progresses in the field, like the identification of modules or recurrent interaction networks, and the use of deep learning algorithms. Two neural network architectures (a RNN and a CNN) are adapted from proteins to RNA. A dataset is created to train these networks: RNANet. Two software tools are proposed: the first is called BiORSEO, which predicts the secondary structure based on two criterions (one relative to the structure’s energy, the other relative to the presence of known modules). The second is MOARNA, which predicts coarse-grained 3D structures based on four criterions: energy in 2D and 3D, compatibility with experimental probing results, and with the shape of a known RNA family if one has been identified
Saffarian, Azadeh. "Algorithmes de prédiction et de recherche de multi-structures d'ARN". Phd thesis, Université des Sciences et Technologie de Lille - Lille I, 2011. http://tel.archives-ouvertes.fr/tel-00832700.
Bedrat, Amina. "G4-Hunter : un nouvel algorithme pour la prédiction des G-quadruplexes". Thesis, Bordeaux, 2015. http://www.theses.fr/2015BORD0197/document.
Biologically relevant G4 DNA structures are formed throughout the genome including immunoglobulin switch regions, promoter sequences and telomeric repeats. They can arise when single-stranded G-rich DNA or RNA sequences are exposed during replication, transcription or recombination. Computational analysis using predictive algorithms suggests that the human genome contains approximately 370 000 potential G4-forming sequences. These predictions are generally limited to the standard G3+N(1−7)G3+N(1−7)G3+N(1−7)G3+ description. However, many stable G4s defy this description and escape this consensus; this is the reason why broadening this description should allow the prediction of more G4 loci. We propose an objective score function, G4- hunter, which predicts G4 folding propensity from a linear nucleic acid sequence. The new method focus on guanines clusters and GC asymmetry, taking into account the whole genomic region rather than individual quadruplexes sequences. In parallel with this computational technique, a large scale in vitro experimental work has also been developed to validate the performance of our algorithm in silico on one hundred of different sequences. G4- hunter exhibits unprecedented accuracy and sensitivity and leads us to reevaluate significantly the number of G4-prone sequences in the human genome. G4-hunter also allowed us to predict potential G4 sequences in HIV and Dictyostelium discoideum, which could not be identified by previous computational methods
Bourquard, Thomas. "Exploitation des algorithmes génétiques pour la prédiction de structures protéine-protéine". Paris 11, 2009. http://www.theses.fr/2009PA112302.
Most proteins fulfill their functions through the interaction with one or many partners as nucleic acids, other proteins…. Because most of these interactions are transitory, they are difficult to detect experimentally and obtaining the structure of the complex is generally not possible. Consequently, “in silico prediction” of the existence of these interactions and of the structure of the resulting complex has received a lot of attention in the last decade. However, proteins are very complex objects, and classical computing approaches have lead to computer-time consuming methods, whose accuracy is not sufficient for large scale exploration of the so-called “interactome” of different organisms. In this context development of high-throughput prediction methods for protein-protein docking is needed. We present here the implementation of a new method based on : Two types of formalisms : the Vornonoi and Laguerre tessellations, two simplified geometric models for coarse-grained modeling of complexes. This leads to computation time more reasonable than in atomic representation, the use and optimization of learning algorithms (genetic algorithms) to isolate the most relevant conformation between two two protein parteners, an evaluation method based on clustering of meta-attributes calculated at the interface to sort the best subset of candidate conformations
Voland, Mathieu. "Algorithmes pour la prédiction in silico d'interactions par similarité entre macromolécules biologiques". Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLV014/document.
The action of a drug, or another small biomolecule, is induced by chemical interactions with other macromolecules such as proteins regulating the cell functions. The determination of the set of targets, the macromolecules that could bind the same small molecule, is essential in order to understand molecular mechanisms responsible for the effects of a drug. Indeed, this knowledge could help the drug design process so as to avoid side effects or to find new applications for known drugs. The advances of structural biology provides us with three-dimensional representations of many proteins involved in these interactions, motivating the use of in silico tools to complement or guide further in vitro or in vivo experiments which are both more expansive and time consuming.This research is conducted as part of a collaboration between the DAVID laboratory of the Versailles-Saint-Quentin University, and Bionext SA which offers a software suite to visualize and analyze chemical interactions between biological molecules. The objective is to design an algorithm to predict these interactions for a given compound, using the structures of potential targets. More precisely, starting from a known interaction between a drug and a protein, a new interaction can be inferred with another sufficiently similar protein. This approach consists in the search of a given pattern, the known binding site, across a collection of macromolecules.An algorithm was implemented, BioBind, which rely on a topological representation of the surface of the macromolecules based on the alpha shapes theory. Our surface representation allows to define a concept of region of any shape on the surface. In order to tackle the search of a given pattern region, a heuristic has been developed, consisting in the definition of regular region which is an approximation of a geodesic disk. This circular shape allows for an exhaustive sampling and fast comparison, and any circular region can then be extended to the actual pattern to provide a similarity evaluation with the query binding site.The target prediction problem is formalized as a binary classification problem, where a set of macromolecules is being separated between those predicted to interact and the others, based on their local similarity with the known target. With this point of view, classic metrics can be used to assess performance, and compare our approach with others. Three datasets were used, two of which were extracted from the literature and the other one was designed specifically for our problem emphasizing the pharmacological relevance of the chosen molecules. Our algorithm proves to be more efficient than another state-of-the-art similarity based approach, and our analysis confirms that docking software are not relevant for our target prediction problem when a first target is known, according to our metric
Dieng, Ibnou. "Prédiction de l'interaction génotype x environnement par linéarisation et régression PLS-mixte". Montpellier 2, 2007. http://www.theses.fr/2007MON20019.
Sànchez, Velazquez Jesús Antonio. "Prédiction et évaluation de performance des algorithmes adaptatifs implantés sur machines parallèles". Paris, ENST, 1993. http://www.theses.fr/1993ENST0021.
Suter, Frédéric. "Parallélisme mixte et prédiction de performances sur réseaux hétérogènes de machines parallèles". Lyon, École normale supérieure (sciences), 2002. http://www.theses.fr/2002ENSL0233.
Capitoli di libri sul tema "Algorithmes de prédiction":
Benbouzid, Bilel. "La régulation juridique de la police prédictive : règles, principes et calculs". In Algorithmes et décisions publiques, 103–24. CNRS Éditions, 2019. http://dx.doi.org/10.4000/books.editionscnrs.46192.