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            "name": "WAVES Training 2018",
            "shortName": "",
            "description": "We organize a workshop to train users to WAVES, a Web Application for Versatile Enhanced Bioinformatic Services",
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            "city": "Paris",
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            "name": "Formation IFB Science Ouverte & PGD - Comment gérer des jeux de données haut-débit en sciences de la vie et de la santé - Session 2",
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            "description": "L’Institut Français de Bioinformatique (IFB) organise une formation à destination de bioinformaticiens, biologistes et médecins. La formation abordera les différents points fondamentaux (théoriques, pratiques, juridiques) en lien avec la politique nationale d’ouverture des données de la recherche et présentera sous forme de séances pratiques les ressources nationales accessibles à la communauté scientifique ainsi que les solutions proposées par l’IFB pour gérer les données d’un projet de recherche.",
            "homepage": "https://www.france-bioinformatique.fr/formation/fair-data-science-ouverte-et-pgd-session-2/",
            "is_draft": false,
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            ],
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            "country": "France",
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            "shortName": "",
            "description": "\n\n\nLes biologistes sont régulièrement confrontés à des gènes (ou des protéines) de fonctions inconnues ou mal annotés. Dans ce contexte, maîtriser quelques techniques basiques d’analyse de séquences peut se révéler d’une aide précieuse. \nL’objectif de cette formation est de présenter, au travers de l’utilisation de sites web spécialisés, quelques grands principes sur l’analyse de séquence. L’ensemble de la formation combine exposés théoriques (fondements méthodologiques des programmes) et applications pratiques (mise en relation des notions théoriques avec les paramètres des programmes et les résultats obtenus) pour permettre une utilisation autonome et critique de quelques logiciels d’analyse des séquences biologiques.\n\n\n\n",
            "homepage": "https://c3bi.pasteur.fr/training-analyse-de-sequences/",
            "is_draft": false,
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                "Sequence analysis",
                "Comparative genomics"
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            "type": "Training course",
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            "name": "Linking gene and function, comparative genomics tools for biologists",
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            "description": "More than twenty years after the first bacterial genome has been sequenced, microbiologists are faced with an avalanche of genomic data. However the quality of the functional annotations of the sequenced proteome is very poor with more than half of the sequenced proteins remaining of unknown function. After taking this course, students should master an array of web-based tools to help to predict gene function. This will allow them to generate in silico based functional predictions and produce illustration for manuscripts that use comparative genomic methods. For background read (https://www.ncbi.nlm.nih.gov/pubmed/20001958)\n",
            "homepage": "https://c3bi.pasteur.fr/training-linking-gene-and-function-comparative-genomics-…",
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            "id": 395,
            "name": "« STATIMAGE», Statistiques pour l’imagerie de microscopie",
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            "homepage": "https://france-bioimaging.org/fr/events/statimage-statistiques-pour-limagerie-de…",
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            "venue": "",
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        },
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            "name": "Bioinformatics of protein--protein interactions for wet lab scientists",
            "shortName": "",
            "description": "Understanding physical and functional interactions between molecules in living systems is crucial in many biological processes. Several powerful methods and techniques have been developed to generate molecular interaction data, focusing mainly on protein­-protein interactions (PPIs). In particular, PPIs involving partially or completely unstructured regions are building blocks of regulatory and signalling networks that control cell response to external and internal cues. Exploring these interactions may help understanding a protein’s function and behavior, predicting biological processes that a protein of unknown function is involved in, and characterising protein complexes that can be used to modulate or perturb known biological processes and pathways.\n",
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            "name": "Cours Programmation Scientifique en Python",
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            "description": "The ever growing usage of high throughput technologies in Biology is revolutionizing the life sciences and profoundly changing its practices. Scripting languages are used on a daily basis in life science labs in order to mine huge data sets produced by high-throughput devices. This two-week course will give participants basic knowledge in python and state-of-the-art machine learning methods to analyze their own data sets.\r\nDescription:\r\nThis course is intended for PhD students, engineers and research scientists willing to acquire knowledge in scientific programming. Throughout the course, we will use Python language to lead participants from the basics of computer programming to more advanced techniques such as practical machine learning techniques.",
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        },
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            "id": 392,
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            "description": "Biological data are often complex and challenging to analyse due to non-normal distributions, nonlinear relationships, spatial/temporal structures and high dimensionality. This course will introduce the students to key concepts and statistical tools for the experimental design and analysis of biological data. After a brief refresher on basic elements of statistics, the students will be made familiar with hypothesis testing, univariate statistical tests (e.g. ANOVA), linear models, descriptive multivariate analyses such as Principal Component Analysis (PCA) and clustering. The course will alternate theoretical aspects and computer exercises on small datasets with the R Studio software. The students will be assigned a small project involving the different concepts and tools covered by the course.\n",
            "homepage": "https://www.enseignement.biologie.ens.fr/spip.php",
            "is_draft": false,
            "costs": [
                "Free"
            ],
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                "Statistical Tests",
                "Regression",
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                "Descriptive statistics",
                "Multivariate analyses"
            ],
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            "updated_at": "2022-06-02T11:50:50.627601Z",
            "type": "Training course",
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            "end_date": "2018-10-11",
            "venue": "",
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        },
        {
            "id": 391,
            "name": "INTRODUCTION À L'ANALYSE DE DONNÉES",
            "shortName": "",
            "description": "\nL'utilisation de plus en plus répandue de techniques d’imagerie et de séquençage à haut-débit en biologie est en train de révolutionner les sciences du vivant et de modifier en profondeur leurs pratiques. Dans ce contexte, des outils statistiques sont développés pour permettre d’analyser ces données de hautes dimensions, et la maîtrise de ces outils devient de plus en plus nécessaire pour produire des résultats de bonne qualité. Ce cours de 4 semaines couvrira les étapes nécessaires pour mettre en place un processus d’analyse de données, depuis la planification de l’expérience jusqu’à la fouille des données en passant par l’échantillonnage, les test d’hypothèses, la modélisation statistique etc.\nCe cours s’adresse en priorité aux étudiants de première année de thèse de l’Institut Pasteur. Tout étudiant en thèse sera automatiquement inscrit à ce cours, mais les élèves de 2e année, de 3e année ou les post-doctorants peuvent également s’inscrire, dans la limite des places disponibles. Il est à noter que le cours est obligatoire pour les étudiants de 1ère année. Des dispenses partielles ou totales sont possibles pour les étudiants qui ont déjà des connaissances en statistique, en mathématique ou en physique. Le cours déroulera sur 4 semaines, 4 jours par semaine, trois heures par jour. Chaque séance de trois heures alternera cours magistral et mise en pratique. Il y aura deux sessions : la première commencera le 22 octobre 2018 et la deuxième le 14 janvier 2019.\nChacune de ces deux sessions sera précédée d’une séance d’introduction à l’informatique. Cette séance proposera des notions d’architecture de l’ordinateur, de système d’organisation des fichiers et de format de fichiers. Chaque session sera également suivie d’un cours optionnel sur l’analyse et le traitement des images.\nPour plus d’information, ainsi que pour les inscriptions au module optionnel et les demandes d’exemption, rendez-vous sur la page du cours : https://c3bi.pasteur.fr/introduction-to-data-analysis-2018-19/\nThèmes abordés\nLe module d’analyse de données couvrira un large champ de notions nécessaires aux étudiants pour planifier leurs expériences, analyser et explorer leurs données, interpréter les résultats et générer des figures à des fins de publication. Il abordera des notions de base en statistique, dont les analyses uni- et multivariées, les analyses descriptives, les distributions statistiques usuelles utilisées en biologie, ainsi que les tests d’hypothèses. Les exercices et travaux pratiques seront réalisés avec R et RStudio. Plusieurs séances seront consacrées à une introduction à l’utilisation du langage de programmation R avant d’aborder les notions de statistiques et d’analyse de données.\nLe module d’analyse d’images introduira les principes de base de l’analyse d’image, et portera plus particulièrement sur l’extraction d’information quantitative d’images de microscopie. Ce cours est destiné aux personnes ayant peu ou pas d’expérience en analyse d’image. Il sera très orienté sur la pratique : des cours magistraux de courte durée seront immédiatement suivis de sessions pratiques. Il aidera à la fois les microscopistes débutants et experts qui n’ont jamais eu de formation concrète en analyse d’image.\n \n\n",
            "homepage": "https://www.pasteur.fr/fr/introduction-data-analysis",
            "is_draft": false,
            "costs": [
                "Free"
            ],
            "topics": [],
            "keywords": [
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                "Statistical Tests",
                "Regression",
                "Dimension reduction",
                "Image analysis",
                "Descriptive statistics",
                "Multivariate analyses"
            ],
            "prerequisites": [
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