Gene expression databases and data mining.

The DNA microarray technology has arguably caught the attention of the worldwide life science community and is now systematically supporting major discoveries in many fields of study. The majority of the initial technical challenges of conducting experiments are being resolved, only to be replaced with new informatics hurdles, including statistical analysis, data visualization, interpretation, and storage. Two systems of databases, one containing expression data and one containing annotation data are quickly becoming essential knowledge repositories of the research community. This present paper surveys several databases, which are considered "pillars" of research and important nodes in the network. This paper focuses on a generalized workflow scheme typical for microarray experiments using two examples related to cancer research. The workflow is used to reference appropriate databases and tools for each step in the process of array experimentation. Additionally, benefits and drawbacks of current array databases are addressed, and suggestions are made for their improvement.

Community Policing in Switzerland's Major Urban Areas: An Observational Study of the Implementation and Impact Using Geospatial Data Mining

La présente étude est à la fois une évaluation du processus de la mise en oeuvre et des impacts de la police de proximité dans les cinq plus grandes zones urbaines de Suisse - Bâle, Berne, Genève, Lausanne et Zurich. La police de proximité (community policing) est à la fois une philosophie et une stratégie organisationnelle qui favorise un partenariat renouvelé entre la police et les communautés locales dans le but de résoudre les problèmes relatifs à la sécurité et à l'ordre public. L'évaluation de processus a analysé des données relatives aux réformes internes de la police qui ont été obtenues par l'intermédiaire d'entretiens semi-structurés avec des administrateurs clés des cinq départements de police, ainsi que dans des documents écrits de la police et d'autres sources publiques. L'évaluation des impacts, quant à elle, s'est basée sur des variables contextuelles telles que des statistiques policières et des données de recensement, ainsi que sur des indicateurs d'impacts construit à partir des données du Swiss Crime Survey (SCS) relatives au sentiment d'insécurité, à la perception du désordre public et à la satisfaction de la population à l'égard de la police. Le SCS est un sondage régulier qui a permis d'interroger des habitants des cinq grandes zones urbaines à plusieurs reprises depuis le milieu des années 1980. L'évaluation de processus a abouti à un « Calendrier des activités » visant à créer des données de panel permettant de mesurer les progrès réalisés dans la mise en oeuvre de la police de proximité à l'aide d'une grille d'évaluation à six dimensions à des intervalles de cinq ans entre 1990 et 2010. L'évaluation des impacts, effectuée ex post facto, a utilisé un concept de recherche non-expérimental (observational design) dans le but d'analyser les impacts de différents modèles de police de proximité dans des zones comparables à travers les cinq villes étudiées. Les quartiers urbains, délimités par zone de code postal, ont ainsi été regroupés par l'intermédiaire d'une typologie réalisée à l'aide d'algorithmes d'apprentissage automatique (machine learning). Des algorithmes supervisés et non supervisés ont été utilisés sur les données à haute dimensionnalité relatives à la criminalité, à la structure socio-économique et démographique et au cadre bâti dans le but de regrouper les quartiers urbains les plus similaires dans des clusters. D'abord, les cartes auto-organisatrices (self-organizing maps) ont été utilisées dans le but de réduire la variance intra-cluster des variables contextuelles et de maximiser simultanément la variance inter-cluster des réponses au sondage. Ensuite, l'algorithme des forêts d'arbres décisionnels (random forests) a permis à la fois d'évaluer la pertinence de la typologie de quartier élaborée et de sélectionner les variables contextuelles clés afin de construire un modèle parcimonieux faisant un minimum d'erreurs de classification. Enfin, pour l'analyse des impacts, la méthode des appariements des coefficients de propension (propensity score matching) a été utilisée pour équilibrer les échantillons prétest-posttest en termes d'âge, de sexe et de niveau d'éducation des répondants au sein de chaque type de quartier ainsi identifié dans chacune des villes, avant d'effectuer un test statistique de la différence observée dans les indicateurs d'impacts. De plus, tous les résultats statistiquement significatifs ont été soumis à une analyse de sensibilité (sensitivity analysis) afin d'évaluer leur robustesse face à un biais potentiel dû à des covariables non observées. L'étude relève qu'au cours des quinze dernières années, les cinq services de police ont entamé des réformes majeures de leur organisation ainsi que de leurs stratégies opérationnelles et qu'ils ont noué des partenariats stratégiques afin de mettre en oeuvre la police de proximité. La typologie de quartier développée a abouti à une réduction de la variance intra-cluster des variables contextuelles et permet d'expliquer une partie significative de la variance inter-cluster des indicateurs d'impacts avant la mise en oeuvre du traitement. Ceci semble suggérer que les méthodes de géocomputation aident à équilibrer les covariables observées et donc à réduire les menaces relatives à la validité interne d'un concept de recherche non-expérimental. Enfin, l'analyse des impacts a révélé que le sentiment d'insécurité a diminué de manière significative pendant la période 2000-2005 dans les quartiers se trouvant à l'intérieur et autour des centres-villes de Berne et de Zurich. Ces améliorations sont assez robustes face à des biais dus à des covariables inobservées et covarient dans le temps et l'espace avec la mise en oeuvre de la police de proximité. L'hypothèse alternative envisageant que les diminutions observées dans le sentiment d'insécurité soient, partiellement, un résultat des interventions policières de proximité semble donc être aussi plausible que l'hypothèse nulle considérant l'absence absolue d'effet. Ceci, même si le concept de recherche non-expérimental mis en oeuvre ne peut pas complètement exclure la sélection et la régression à la moyenne comme explications alternatives. The current research project is both a process and impact evaluation of community policing in Switzerland's five major urban areas - Basel, Bern, Geneva, Lausanne, and Zurich. Community policing is both a philosophy and an organizational strategy that promotes a renewed partnership between the police and the community to solve problems of crime and disorder. The process evaluation data on police internal reforms were obtained through semi-structured interviews with key administrators from the five police departments as well as from police internal documents and additional public sources. The impact evaluation uses official crime records and census statistics as contextual variables as well as Swiss Crime Survey (SCS) data on fear of crime, perceptions of disorder, and public attitudes towards the police as outcome measures. The SCS is a standing survey instrument that has polled residents of the five urban areas repeatedly since the mid-1980s. The process evaluation produced a "Calendar of Action" to create panel data to measure community policing implementation progress over six evaluative dimensions in intervals of five years between 1990 and 2010. The impact evaluation, carried out ex post facto, uses an observational design that analyzes the impact of the different community policing models between matched comparison areas across the five cities. Using ZIP code districts as proxies for urban neighborhoods, geospatial data mining algorithms serve to develop a neighborhood typology in order to match the comparison areas. To this end, both unsupervised and supervised algorithms are used to analyze high-dimensional data on crime, the socio-economic and demographic structure, and the built environment in order to classify urban neighborhoods into clusters of similar type. In a first step, self-organizing maps serve as tools to develop a clustering algorithm that reduces the within-cluster variance in the contextual variables and simultaneously maximizes the between-cluster variance in survey responses. The random forests algorithm then serves to assess the appropriateness of the resulting neighborhood typology and to select the key contextual variables in order to build a parsimonious model that makes a minimum of classification errors. Finally, for the impact analysis, propensity score matching methods are used to match the survey respondents of the pretest and posttest samples on age, gender, and their level of education for each neighborhood type identified within each city, before conducting a statistical test of the observed difference in the outcome measures. Moreover, all significant results were subjected to a sensitivity analysis to assess the robustness of these findings in the face of potential bias due to some unobserved covariates. The study finds that over the last fifteen years, all five police departments have undertaken major reforms of their internal organization and operating strategies and forged strategic partnerships in order to implement community policing. The resulting neighborhood typology reduced the within-cluster variance of the contextual variables and accounted for a significant share of the between-cluster variance in the outcome measures prior to treatment, suggesting that geocomputational methods help to balance the observed covariates and hence to reduce threats to the internal validity of an observational design. Finally, the impact analysis revealed that fear of crime dropped significantly over the 2000-2005 period in the neighborhoods in and around the urban centers of Bern and Zurich. These improvements are fairly robust in the face of bias due to some unobserved covariate and covary temporally and spatially with the implementation of community policing. The alternative hypothesis that the observed reductions in fear of crime were at least in part a result of community policing interventions thus appears at least as plausible as the null hypothesis of absolutely no effect, even if the observational design cannot completely rule out selection and regression to the mean as alternative explanations.

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.

Histology-driven data mining of lipid signatures from multiple imaging mass spectrometry analyses: application to human colorectal cancer liver metastasis biopsies.

Imaging mass spectrometry (IMS) represents an innovative tool in the cancer research pipeline, which is increasingly being used in clinical and pharmaceutical applications. The unique properties of the technique, especially the amount of data generated, make the handling of data from multiple IMS acquisitions challenging. This work presents a histology-driven IMS approach aiming to identify discriminant lipid signatures from the simultaneous mining of IMS data sets from multiple samples. The feasibility of the developed workflow is evaluated on a set of three human colorectal cancer liver metastasis (CRCLM) tissue sections. Lipid IMS on tissue sections was performed using MALDI-TOF/TOF MS in both negative and positive ionization modes after 1,5-diaminonaphthalene matrix deposition by sublimation. The combination of both positive and negative acquisition results was performed during data mining to simplify the process and interrogate a larger lipidome into a single analysis. To reduce the complexity of the IMS data sets, a sub data set was generated by randomly selecting a fixed number of spectra from a histologically defined region of interest, resulting in a 10-fold data reduction. Principal component analysis confirmed that the molecular selectivity of the regions of interest is maintained after data reduction. Partial least-squares and heat map analyses demonstrated a selective signature of the CRCLM, revealing lipids that are significantly up- and down-regulated in the tumor region. This comprehensive approach is thus of interest for defining disease signatures directly from IMS data sets by the use of combinatory data mining, opening novel routes of investigation for addressing the demands of the clinical setting.

Intégration de méthodes de data mining dans le renseignement criminel : analyse par des structures issues de la théorie des graphes dans le profilage des stupéfiants

Data mining can be defined as the extraction of previously unknown and potentially useful information from large datasets. The main principle is to devise computer programs that run through databases and automatically seek deterministic patterns. It is applied in different fields of application, e.g., remote sensing, biometry, speech recognition, but has seldom been applied to forensic case data. The intrinsic difficulty related to the use of such data lies in its heterogeneity, which comes from the many different sources of information. The aim of this study is to highlight potential uses of pattern recognition that would provide relevant results from a criminal intelligence point of view. The role of data mining within a global crime analysis methodology is to detect all types of structures in a dataset. Once filtered and interpreted, those structures can point to previously unseen criminal activities. The interpretation of patterns for intelligence purposes is the final stage of the process. It allows the researcher to validate the whole methodology and to refine each step if necessary. An application to cutting agents found in illicit drug seizures was performed. A combinatorial approach was done, using the presence and the absence of products. Methods coming from the graph theory field were used to extract patterns in data constituted by links between products and place and date of seizure. A data mining process completed using graphing techniques is called ``graph mining''. Patterns were detected that had to be interpreted and compared with preliminary knowledge to establish their relevancy. The illicit drug profiling process is actually an intelligence process that uses preliminary illicit drug classes to classify new samples. Methods proposed in this study could be used \textit{a priori} to compare structures from preliminary and post-detection patterns. This new knowledge of a repeated structure may provide valuable complementary information to profiling and become a source of intelligence.

Des données aux connaissances, un chemin difficile: réflexion sur la place du data mining en analyse criminelle

Le "data mining", ou "fouille de données", est un ensemble de méthodes et de techniques attractif qui a connu une popularité fulgurante ces dernières années, spécialement dans le domaine du marketing. Le développement récent de l'analyse ou du renseignement criminel soulève des problèmatiques auxqwuelles il est tentant de d'appliquer ces méthodes et techniques. Le potentiel et la place du data mining dans le contexte de l'analyse criminelle doivent être mieux définis afin de piloter son application. Cette réflexion est menée dans le cadre du renseignement produit par des systèmes de détection et de suivi systématique de la criminalité répétitive, appelés processus de veille opérationnelle. Leur fonctionnement nécessite l'existence de patterns inscrits dans les données, et justifiés par les approches situationnelles en criminologie. Muni de ce bagage théorique, l'enjeu principal revient à explorer les possibilités de détecter ces patterns au travers des méthodes et techniques de data mining. Afin de répondre à cet objectif, une recherche est actuellement menée au Suisse à travers une approche interdisciplinaire combinant des connaissances forensiques, criminologiques et computationnelles.

Health literacy [Document électronique] : an economic perspective and data for Switzerland. Part 2, A review of health literacy measures and a cost assessment of limited health literacy

Summary : 1. Measuring health literacy in Switzerland: a review of six surveys: 1.1 Comparison of questionnaires - 1.2 Measures of health literacy in Switzerland - 1.3 Discussion of Swiss data on HL - 1.4 Description of the six surveys: 1.4.1 Current health trends and health literacy in the Swiss population (gfs-UNIVOX), 1.4.2 Nutrition, physical exercise and body weight : opinions and perceptions of the Swiss population (USI), 1.4.3 Health Literacy in Switzerland (ISPMZ), 1.4.4 Swiss Health Survey (SHS), 1.4.5 Survey of Health, Ageing and Retirement in Europe (SHARE), 1.4.6 Adult literacy and life skills survey (ALL). - 2 . Economic costs of low health literacy in Switzerland: a rough calculation. Appendix: Screenshots cost model

Hyperhidrosis: a new and often early symptom in Fabry disease. International experience and data from the Fabry Outcome Survey.

Hypohidrosis is a classic feature of Fabry disease; in contrast, hyperhidrosis has only been rarely described. The aim of the study is to characterise the baseline descriptive data on hyperhidrosis (frequency, age at onset, sex ratio and outcome with and without enzyme replacement therapy) in hemizygous male and heterozygous female patients with Fabry disease. We describe case histories of five patients with Fabry disease and hyperhidrosis seen at three different centres. We have also analysed a cohort of 21 paediatric patients in the UK and a large European cohort of patients enrolled in the Fabry Outcome Survey (FOS). Five patients (three female, two male) with hyperhidrosis were originally identified, although each had additional symptoms related to Fabry disease. The age at onset of hyperhidrosis was less than 18 years in four cases. In the cohort of 21 paediatric patients (12 female, nine male), one female had hyperhidrosis; the age at onset of this symptom was 11 years. In the FOS cohort, 66 of 714 patients with Fabry disease had hyperhidrosis (44 of 369 females, 11.9%; 22 of 345 males, 6.4%). The female predominance was observed in seven of nine countries from which data were analysed. Hyperhidrosis is an increasingly recognised feature of the Fabry disease phenotype. It is more prevalent in females than in males and often appears in childhood or adolescence. The efficacy of enzyme replacement therapy on this recently recognised symptom should be assessed.

From Translation to Rewriting : the Interplay of Text and Image in The Fairy Tales of Charles Perrault and The Bloody Chamber and Other Stories

Abstract :This article examines the interplay of text and image in The Fairy Tales of Charles Perrault (1977), translated by Angela Carter and illustrated by Martin Ware, as a form of intersemiotic dialogue that sheds new light on Carter's work. It argues that Ware's highly original artwork based on the translation not only calls into question the association of fairy tales with children's literature (which still characterizes Carter's translation), but also captures an essential if heretofore neglected aspect of Carter's creative process, namely the dynamics between translating, illustrating and rewriting classic tales. Several elements from Ware's illustrations are indeed taken up and elaborated on in The Bloody Chamber and Other Stories (1979), the collection of "stories about fairy stories" that made Carter famous. These include visual details and strategies that she transposed to the realm of writing, giving rise to reflections on the relation between visuality and textuality.RésuméCet article considère l'interaction du texte et de l'image dans les contes de Perrault traduits par Angela Carter et illustrés par Martin Ware (The Fairy Tales of Charles Perrault, 1977) comme une forme de dialogue intersémiotique particulièrement productif. Il démontre que les illustrations originales de Ware ne mettent pas seulement en question l'assimilation des contes à la littérature de jeunesse (qui est encore la perspective adoptée par la traductrice dans ce livre), mais permettent aussi de saisir un aspect essentiel bien que jusque là ignoré du procession de création dans l'oeuvre de Carter, à savoir la dynamique qui lie la traduction, l'illustration et la réécriture des contes classiques. Plusieurs éléments des illustrations de Ware sont ainsi repris et élaborés dans The Bloody Chamber and Other Stories (1979), la collection de "stories about fairy stories" qui rendit Carter célèbre. La transposition de détails et de stratégies visuelles dans l'écriture donnent ainsi l'occasion de réflexions sur les rapports entre la visualité et la textualité.

Mapping of Environmental Data Using Kernel-Based Methods

Recently, kernel-based Machine Learning methods have gained great popularity in many data analysis and data mining fields: pattern recognition, biocomputing, speech and vision, engineering, remote sensing etc. The paper describes the use of kernel methods to approach the processing of large datasets from environmental monitoring networks. Several typical problems of the environmental sciences and their solutions provided by kernel-based methods are considered: classification of categorical data (soil type classification), mapping of environmental and pollution continuous information (pollution of soil by radionuclides), mapping with auxiliary information (climatic data from Aral Sea region). The promising developments, such as automatic emergency hot spot detection and monitoring network optimization are discussed as well.