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Academic literature on the topic 'Recherche d’anomalies'
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Journal articles on the topic "Recherche d’anomalies"
Dewolf, C., B. Duron, and G. Loas. "Recherche d’anomalies électroencéphalographiques chez les patients psychopathes ; étude cas-témoins." Annales Médico-psychologiques, revue psychiatrique 160, no. 5-6 (July 2002): 451–55. http://dx.doi.org/10.1016/s0003-4487(02)00199-3.
Full textHostein, I., P. P. Bringuier, J. Y. Scoazec, J. F. Emile, and J. M. Coindre. "Recherche d’anomalies moléculaires dans les GIST non mutées pour KIT et PDGFRA." Revue de Chirurgie Orthopédique et Réparatrice de l'Appareil Moteur 93, no. 7 (November 2007): 757. http://dx.doi.org/10.1016/s0035-1040(07)73274-9.
Full textDavid, A., A. Fernandez, M. L. Menard, E. Dor, G. Leali, F. Maria, C. Cruzel, F. Askenazy, C. Verstuyft, and S. Thümmler. "La pharmacorésistance aux psychotropes chez l’enfant et l’adolescent : recherche d’anomalies pharmacogénétiques du cytochrome P450 2D6." L'Encéphale 47, no. 3 (June 2021): 227–34. http://dx.doi.org/10.1016/j.encep.2020.09.010.
Full textDUCOS, A., H. M. BERLAND, A. PINTON, A. SEGUELA, C. BRUN-BARONNAT, A. DARRE, and R. DARRE. "Contrôle chromosomique des populations animales d’élevage." INRAE Productions Animales 13, no. 1 (February 18, 2000): 25–35. http://dx.doi.org/10.20870/productions-animales.2000.13.1.3765.
Full textGuedj, Myriam, Marion Rosier, Patrick Calvas, Sophie Julia, Christelle Garnier, Anne Cambon thomsen, and Maria Teresa Munoz Sastre. "Chapitre 8. Annoncer ou pas la découverte d’anomalies non sollicitées lors d’un test génétique à séquençage haut débit ?" Journal international de bioéthique et d'éthique des sciences Vol. 34, no. 2 (August 28, 2023): 121–30. http://dx.doi.org/10.3917/jibes.342.0121.
Full textBOURDON, L., E. SAGUI, N. MONNIER, N. KOULMANN, F. CANINI, C. BROSSET, and J. LUNARDI. "Coup de chaleur d’exercice, hyperthermie maligne per-anesthésique et pathologies musculaires." Médecine et Armées Vol. 40 No. 3, Volume 40, Numéro 3 (June 1, 2012): 217–24. http://dx.doi.org/10.17184/eac.6609.
Full textBoulestreau, R., F. Jambon, A. Cremer, J. Doublet, M. L. Nunes, A. Ferrière, A. Tabarin, M. Haissaguerre, and P. Gosse. "Apport du 2D strain et des outils échocardiographiques classiques pour la recherche d’anomalies myocardiques induites par l’exposition chronique à un phéochromocytome." Annales de Cardiologie et d'Angéiologie 69, no. 5 (November 2020): 241–46. http://dx.doi.org/10.1016/j.ancard.2020.09.010.
Full textDavid, A., A. Fernandez, M. L. Menard, E. Dor, G. Leali, F. Maria, E. Fontas, F. Askenazy, C. Verstuyft, and S. Thümmler. "Pharmacorésistance aux psychotropes chez l’enfant et l’adolescent et recherche d’anomalies pharmacogénétiques du cytochrome P450 2D6. Vers une médecine personnalisée en pédopsychiatrie ?" French Journal of Psychiatry 1 (December 2019): S147. http://dx.doi.org/10.1016/j.fjpsy.2019.10.411.
Full textConsoli, S. M. "Devenir des troubles somatoformes dans le DSM-5 : disparition ou renouveau ?" European Psychiatry 30, S2 (November 2015): S41. http://dx.doi.org/10.1016/j.eurpsy.2015.09.116.
Full textMoshynskyy, Anton, Keren-happuch Ho, and Gudrun Caspar-Bell. "A Case of Autoimmune Polyglandular Syndrome Type 2 Presenting as Dyspnea." Canadian Journal of General Internal Medicine 16, no. 4 (December 14, 2021): 13–16. http://dx.doi.org/10.22374/cjgim.v16i4.508.
Full textDissertations / Theses on the topic "Recherche d’anomalies"
Linardi, Michele. "Variable-length similarity search for very large data series : subsequence matching, motif and discord detection." Electronic Thesis or Diss., Sorbonne Paris Cité, 2019. http://www.theses.fr/2019USPCB056.
Full textData series (ordered sequences of real valued points, a.k.a. time series) has become one of the most important and popular data-type, which is present in almost all scientific fields. For the last two decades, but more evidently in this last period the interest in this data-type is growing at a fast pace. The reason behind this is mainly due to the recent advances in sensing, networking, data processing and storage technologies, which have significantly assisted the process of generating and collecting large amounts of data series. Data series similarity search has emerged as a fundamental operation at the core of several analysis tasks and applications related to data series collections. Many solutions to different data mining problems, such as Clustering, Subsequence Matching, Imputation of Missing Values, Motif Discovery, and Anomaly detection work by means of similarity search. Data series indexes have been proposed for fast similarity search. Nevertheless all existing indexes can only answer queries of a single length (fixed at index construction time), which is a severe limitation. In this regard, all solutions for the aforementioned problems require the prior knowledge of the series length, on which similarity search is performed. Consequently, the user must know the length of the expected results, which is often an unrealistic assumption. This aspect is thus of paramount importance. In several cases, the length is a critical parameter that heavily influences the quality of the final outcome. In this thesis, we propose scalable solutions that enable variable-length analysis of very large data series collections. We propose ULISSE, the first data series index structure designed for answering similarity search queries of variable length. Our contribution is two-fold. First, we introduce a novel representation technique, which effectively and succinctly summarizes multiple sequences of different length. Based on the proposed index, we describe efficient algorithms for approximate and exact similarity search, combining disk based index visits and in-memory sequential scans. Our approach supports non Z-normalized and Z-normalized sequences, and can be used with no changes with both Euclidean Distance and Dynamic Time Warping, for answering both κ-NN and ε-range queries. We experimentally evaluate our approach using several synthetic and real datasets. The results show that ULISSE is several times, and up to orders of magnitude more efficient in terms of both space and time cost, when compared to competing approaches. Subsequently, we introduce a new framework, which provides an exact and scalable motif and discord discovery algorithm that efficiently finds all motifs and discords in a given range of lengths. The experimental evaluation we conducted over several diverse real datasets show that our approaches are up to orders of magnitude faster than the alternatives. We moreover demonstrate that we can remove the unrealistic constraint of performing analytics using a predefined length, leading to more intuitive and actionable results, which would have otherwise been missed