The Brain Imaging Data Structure, a Format for Organizing and Describing Outputs of Neuroimaging Experiments

The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations

Linguistic annotation in/for corpus linguistics

This article surveys linguistic annotation in corpora and corpus linguistics. We first define the concept of 'corpus ' as a radial category and then, in Section 2, discuss a variety of kinds of information for which corpora are annotated and that are exploited in contemporary corpus linguistics. Section 3 then exemplifies many current formats of annotation with an eye to highlighting both the diversity of formats currently available and the emergence of XML annotation as, for now, the most widespread form of annotation. Section 4 summarizes and concludes with desiderata for future developments.

A Survey on Mapping Semi-Structured Data and Graph Data to Relational Data

The data produced by various services should be stored and managed in an appropriate format for gaining valuable knowledge conveniently. This leads to the emergence of various data models, including relational, semi-structured, and graph models, and so on. Considering the fact that the mature relational databases established on relational data models are still predominant in today's market, it has fueled interest in storing and processing semi-structured data and graph data in relational databases so that mature and powerful relational databases' capabilities can all be applied to these various data. In this survey, we review existing methods on mapping semi-structured data and graph data into relational tables, analyze their major features, and give a detailed classification of those methods. We also summarize the merits and demerits of each method, introduce open research challenges, and present future research directions. With this comprehensive investigation of existing methods and open problems, we hope this survey can motivate new mapping approaches through drawing lessons from eachmodel's mapping strategies, aswell as a newresearch topic - mapping multi-model data into relational tables.Peer reviewe

Automatic reconstruction of itineraries from descriptive texts

Esta tesis se inscribe dentro del marco del proyecto PERDIDO donde los objetivos son la extracción y reconstrucción de itinerarios a partir de documentos textuales. Este trabajo se ha realizado en colaboración entre el laboratorio LIUPPA de l' Université de Pau et des Pays de l' Adour (France), el grupo de Sistemas de Información Avanzados (IAAA) de la Universidad de Zaragoza y el laboratorio COGIT de l' IGN (France). El objetivo de esta tesis es concebir un sistema automático que permita extraer, a partir de guías de viaje o descripciones de itinerarios, los desplazamientos, además de representarlos sobre un mapa. Se propone una aproximación para la representación automática de itinerarios descritos en lenguaje natural. Nuestra propuesta se divide en dos tareas principales. La primera pretende identificar y extraer de los textos describiendo itinerarios información como entidades espaciales y expresiones de desplazamiento o percepción. El objetivo de la segunda tarea es la reconstrucción del itinerario. Nuestra propuesta combina información local extraída gracias al procesamiento del lenguaje natural con datos extraídos de fuentes geográficas externas (por ejemplo, gazetteers). La etapa de anotación de informaciones espaciales se realiza mediante una aproximación que combina el etiquetado morfo-sintáctico y los patrones léxico-sintácticos (cascada de transductores) con el fin de anotar entidades nombradas espaciales y expresiones de desplazamiento y percepción. Una primera contribución a la primera tarea es la desambiguación de topónimos, que es un problema todavía mal resuelto dentro del reconocimiento de entidades nombradas (Named Entity Recognition - NER) y esencial en la recuperación de información geográfica. Se plantea un algoritmo no supervisado de georreferenciación basado en una técnica de clustering capaz de proponer una solución para desambiguar los topónimos los topónimos encontrados en recursos geográficos externos, y al mismo tiempo, la localización de topónimos no referenciados. Se propone un modelo de grafo genérico para la reconstrucción automática de itinerarios, donde cada nodo representa un lugar y cada arista representa un camino enlazando dos lugares. La originalidad de nuestro modelo es que además de tener en cuenta los elementos habituales (caminos y puntos del recorrido), permite representar otros elementos involucrados en la descripción de un itinerario, como por ejemplo los puntos de referencia visual. Se calcula de un árbol de recubrimiento mínimo a partir de un grafo ponderado para obtener automáticamente un itinerario bajo la forma de un grafo. Cada arista del grafo inicial se pondera mediante un método de análisis multicriterio que combina criterios cualitativos y cuantitativos. El valor de estos criterios se determina a partir de informaciones extraídas del texto e informaciones provenientes de recursos geográficos externos. Por ejemplo, se combinan las informaciones generadas por el procesamiento del lenguaje natural como las relaciones espaciales describiendo una orientación (ej: dirigirse hacia el sur) con las coordenadas geográficas de lugares encontrados dentro de los recursos para determinar el valor del criterio ``relación espacial''. Además, a partir de la definición del concepto de itinerario y de las informaciones utilizadas en la lengua para describir un itinerario, se ha modelado un lenguaje de anotación de información espacial adaptado a la descripción de desplazamientos, apoyándonos en las recomendaciones del consorcio TEI (Text Encoding and Interchange). Finalmente, se ha implementado y evaluado las diferentes etapas de nuestra aproximación sobre un corpus multilingüe de descripciones de senderos y excursiones (francés, español, italiano)

Development of a web-based platform for Biomedical Text Mining

Dissertação de mestrado em Engenharia InformáticaBiomedical Text Mining (BTM) seeks to derive high-quality information from literature in the biomedical domain, by creating tools/methodologies that can automate time-consuming tasks when searching for new information. This encompasses both Information Retrieval, the discovery and recovery of relevant documents, and Information Extraction, the capability to extract knowledge from text. In the last years, SilicoLife, with the collaboration of the University of Minho, has been developing @Note2, an open-source Java-based multiplatform BTM workbench, including libraries to perform the main BTM tasks, also provid ing user-friendly interfaces through a stand-alone application. This work addressed the development of a web-based software platform that is able to address some of the main tasks within BTM, supported by the existing core libraries from the @Note project. This included the improvement of the available RESTful server, providing some new methods and APIs, and improving others, while also developing a web-based application through calls to the API provided by the server and providing a functional user-friendly web-based interface. This work focused on the development of tasks related with Information Retrieval, addressing the efficient search of relevant documents through an integrated interface. Also, at this stage the aim was to have interfaces to visualize and explore the main entities involved in BTM: queries, documents, corpora, annotation processes entities and resources.A mineração de Literatura Biomédica (BioLM) pretende extrair informação de alta qualidade da área biomédica, através da criação de ferramentas/metodologias que consigam automatizar tarefas com elevado dispêndio de tempo. As tarefas subjacentes vão desde recuperação de informação, descoberta e recuperação de documentos relevantes para a extração de informação pertinente e a capacidade de extrair conhecimento de texto. Nos últimos anos a SilicoLife tem vindo a desenvolver uma ferramenta, o @Note2, uma BioLM Workbench multiplataforma baseada em JAVA, que executa as principais tarefas inerentes a BioLM. Também possui uma versão autónoma com uma interface amigável para o utilizador. Esta tese desenvolveu uma plataforma de software baseada na web, que é capaz de executar algumas das tarefas de BioLM, com suporte num núcleo de bibliotecas do projeto @Note. Para tal foi necessário melhorar o servidor RESTfid atual, criando novos métodos e APIs, como também desenvolver a aplicação baseada na web, com uma interface amigável para o utilizador, que comunicará com o servidor através de chamadas à sua APL Este trabalho focou o seu desenvolvimento em tarefas relacionadas com recuperação de informação, focando na pesquisa eficiente de documentos de interesse através de uma interface integrada. Nesta fase, o objetivo foi também ter um conjunto de interfaces capazes de visualizar e explorar as principais entidades envolvidas em BioLM: pesquisas, documentos, corpora, entidades relacionadas com processos de anotações e recursos

Discovering lesser known molecular players and mechanistic patterns in Alzheimer's disease using an integrative disease modelling approach

Convergence of exponentially advancing technologies is driving medical research with life changing discoveries. On the contrary, repeated failures of high-profile drugs to battle Alzheimer's disease (AD) has made it one of the least successful therapeutic area. This failure pattern has provoked researchers to grapple with their beliefs about Alzheimer's aetiology. Thus, growing realisation that Amyloid-β and tau are not 'the' but rather 'one of the' factors necessitates the reassessment of pre-existing data to add new perspectives. To enable a holistic view of the disease, integrative modelling approaches are emerging as a powerful technique. Combining data at different scales and modes could considerably increase the predictive power of the integrative model by filling biological knowledge gaps. However, the reliability of the derived hypotheses largely depends on the completeness, quality, consistency, and context-specificity of the data. Thus, there is a need for agile methods and approaches that efficiently interrogate and utilise existing public data. This thesis presents the development of novel approaches and methods that address intrinsic issues of data integration and analysis in AD research. It aims to prioritise lesser-known AD candidates using highly curated and precise knowledge derived from integrated data. Here much of the emphasis is put on quality, reliability, and context-specificity. This thesis work showcases the benefit of integrating well-curated and disease-specific heterogeneous data in a semantic web-based framework for mining actionable knowledge. Furthermore, it introduces to the challenges encountered while harvesting information from literature and transcriptomic resources. State-of-the-art text-mining methodology is developed to extract miRNAs and its regulatory role in diseases and genes from the biomedical literature. To enable meta-analysis of biologically related transcriptomic data, a highly-curated metadata database has been developed, which explicates annotations specific to human and animal models. Finally, to corroborate common mechanistic patterns — embedded with novel candidates — across large-scale AD transcriptomic data, a new approach to generate gene regulatory networks has been developed. The work presented here has demonstrated its capability in identifying testable mechanistic hypotheses containing previously unknown or emerging knowledge from public data in two major publicly funded projects for Alzheimer's, Parkinson's and Epilepsy diseases