Anne Laurent

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The human insulin-family genes regulate cell growth, metabolism, and tissue-specific functions. Among these different members, only INSL4 gene shows a predominant placenta-specific expression. Here, we show that the human INSL4 gene is tightly clustered with three other members of the human insulin superfamily (RLN1, RLN2, and INSL6) within a 176-kilobase(More)
Multidimensional databases have been designed to provide decision makers with the necessary tools to help them understand their data. This framework is different from transactional data as the datasets contain huge volumes of historicized and aggregated data defined over a set of dimensions that can be arranged through multiple levels of granularities. Many(More)
Mining gradual rules plays a crucial role in many real world applications where huge volumes of complex numerical data must be handled, e.g., biological databases, survey databases, data streams or sensor readings. Gradual rules highlight complex order correlations of the form “The more/less X, then the more/less Y ”. Such rules have been studied since the(More)
Data aggregation is one of the key features used in databases, especially for Business Intelligence (e.g., ETL, OLAP) and analytics/data mining. When considering SQL databases, aggregation is used to prepare and visualize data for deeper analyses. However, these operations are often impossible on very large volumes of data regarding(More)
Text categorization is a well-known task based essentially on statistical approaches using neural networks, Support Vector Machines and other machine learning algorithms. Texts are generally considered as bags of words without any order. Although these approaches have proven to be efficient, they do not provide users with comprehensive and reusable rules(More)
Even if they have proven to be relevant on traditional transactional databases, data mining tools are still inefficient on some kinds of databases. In particular, databases containing discrete values or having a value for each item, like gene expression data, are especially challenging. On such data, existing approaches either transform the data to(More)
Mining frequent patterns from huge databases have been addressed for many years and results have been applied to many fields, including banking, marketing, biology, health, etc. Fuzzy approaches have been proposed in order to soften the constraints on the patterns found by the algorithms. However, when dealing with complex databases such as tree databases(More)
Résumé. La recherche d’un schéma médiateur à partir d’un ensemble de schémas XML est une problématique actuelle où les résultats de recherche issus de la fouille de données arborescentes peuvent être adoptés. Dans ce contexte, plusieurs propositions ont été réalisées mais les méthodes de représentation des arborescences sont souvent trop coûteuses pour(More)
Despite the existence of different methods, including data mining techniques, available to be used in recommender systems, such systems still contain numerous limitations. They are in a constant need for personalization in order to make effective suggestions and to provide valuable information of items available. A way to reach such personalization is by(More)
Most real world databases consist of historical and numerical data such as sensor, scientific or even demographic data. In this context, classical algorithms extracting sequential patterns, which are well adapted to the temporal aspect of data, do not allow numerical information processing. Therefore, the data are pre-processed to be transformed into a(More)