3,704 research outputs found
A Query Integrator and Manager for the Query Web
We introduce two concepts: the Query Web as a layer of interconnected queries over the document web and the semantic web, and a Query Web Integrator and Manager (QI) that enables the Query Web to evolve. QI permits users to write, save and reuse queries over any web accessible source, including other queries saved in other installations of QI. The saved queries may be in any language (e.g. SPARQL, XQuery); the only condition for interconnection is that the queries return their results in some form of XML. This condition allows queries to chain off each other, and to be written in whatever language is appropriate for the task. We illustrate the potential use of QI for several biomedical use cases, including ontology view generation using a combination of graph-based and logical approaches, value set generation for clinical data management, image annotation using terminology obtained from an ontology web service, ontology-driven brain imaging data integration, small-scale clinical data integration, and wider-scale clinical data integration. Such use cases illustrate the current range of applications of QI and lead us to speculate about the potential evolution from smaller groups of interconnected queries into a larger query network that layers over the document and semantic web. The resulting Query Web could greatly aid researchers and others who now have to manually navigate through multiple information sources in order to answer specific questions
PubMed and Beyond: Recent Advances and Best Practices in Biomedical Literature Search
Biomedical research yields a wealth of information, much of which is only
accessible through the literature. Consequently, literature search is an
essential tool for building on prior knowledge in clinical and biomedical
research. Although recent improvements in artificial intelligence have expanded
functionality beyond keyword-based search, these advances may be unfamiliar to
clinicians and researchers. In response, we present a survey of literature
search tools tailored to both general and specific information needs in
biomedicine, with the objective of helping readers efficiently fulfill their
information needs. We first examine the widely used PubMed search engine,
discussing recent improvements and continued challenges. We then describe
literature search tools catering to five specific information needs: 1.
Identifying high-quality clinical research for evidence-based medicine. 2.
Retrieving gene-related information for precision medicine and genomics. 3.
Searching by meaning, including natural language questions. 4. Locating related
articles with literature recommendation. 5. Mining literature to discover
associations between concepts such as diseases and genetic variants.
Additionally, we cover practical considerations and best practices for choosing
and using these tools. Finally, we provide a perspective on the future of
literature search engines, considering recent breakthroughs in large language
models such as ChatGPT. In summary, our survey provides a comprehensive view of
biomedical literature search functionalities with 36 publicly available tools.Comment: 27 pages, 6 figures, 36 tool
MBAT: A scalable informatics system for unifying digital atlasing workflows
Abstract Background Digital atlases provide a common semantic and spatial coordinate system that can be leveraged to compare, contrast, and correlate data from disparate sources. As the quality and amount of biological data continues to advance and grow, searching, referencing, and comparing this data with a researcher's own data is essential. However, the integration process is cumbersome and time-consuming due to misaligned data, implicitly defined associations, and incompatible data sources. This work addressing these challenges by providing a unified and adaptable environment to accelerate the workflow to gather, align, and analyze the data. Results The MouseBIRN Atlasing Toolkit (MBAT) project was developed as a cross-platform, free open-source application that unifies and accelerates the digital atlas workflow. A tiered, plug-in architecture was designed for the neuroinformatics and genomics goals of the project to provide a modular and extensible design. MBAT provides the ability to use a single query to search and retrieve data from multiple data sources, align image data using the user's preferred registration method, composite data from multiple sources in a common space, and link relevant informatics information to the current view of the data or atlas. The workspaces leverage tool plug-ins to extend and allow future extensions of the basic workspace functionality. A wide variety of tool plug-ins were developed that integrate pre-existing as well as newly created technology into each workspace. Novel atlasing features were also developed, such as supporting multiple label sets, dynamic selection and grouping of labels, and synchronized, context-driven display of ontological data. Conclusions MBAT empowers researchers to discover correlations among disparate data by providing a unified environment for bringing together distributed reference resources, a user's image data, and biological atlases into the same spatial or semantic context. Through its extensible tiered plug-in architecture, MBAT allows researchers to customize all platform components to quickly achieve personalized workflows
SaDA: From Sampling to Data Analysis—An Extensible Open Source Infrastructure for Rapid, Robust and Automated Management and Analysis of Modern Ecological High-Throughput Microarray Data
One of the most crucial characteristics of day-to-day laboratory information management is the collection, storage and retrieval of information about research subjects and environmental or biomedical samples. An efficient link between sample data and experimental results is absolutely important for the successful outcome of a collaborative project. Currently available software solutions are largely limited to large scale, expensive commercial Laboratory Information Management Systems (LIMS). Acquiring such LIMS indeed can bring laboratory information management to a higher level, but most of the times this requires a sufficient investment of money, time and technical efforts. There is a clear need for a light weighted open source system which can easily be managed on local servers and handled by individual researchers. Here we present a software named SaDA for storing, retrieving and analyzing data originated from microorganism monitoring experiments. SaDA is fully integrated in the management of environmental samples, oligonucleotide sequences, microarray data and the subsequent downstream analysis procedures. It is simple and generic software, and can be extended and customized for various environmental and biomedical studies
Semantic wikis as flexible database interfaces for biomedical applications
Several challenges prevent extracting knowledge from biomedical resources, including data heterogeneity and the difficulty to obtain and collaborate on data and annotations by medical doctors. Therefore, flexibility in their representation and interconnection is required; it is also essential to be able to interact easily with such data. In recent years, semantic tools have been developed: semantic wikis are collections of wiki pages that can be annotated with properties and so combine flexibility and expressiveness, two desirable aspects when modeling databases, especially in the dynamic biomedical domain. However, semantics and collaborative analysis of biomedical data is still an unsolved challenge. The aim of this work is to create a tool for easing the design and the setup of semantic databases and to give the possibility to enrich them with biostatistical applications. As a side effect, this will also make them reproducible, fostering their application by other research groups. A command-line software has been developed for creating all structures required by Semantic MediaWiki. Besides, a way to expose statistical analyses as R Shiny applications in the interface is provided, along with a facility to export Prolog predicates for reasoning with external tools. The developed software allowed to create a set of biomedical databases for the Neuroscience Department of the University of Padova in a more automated way. They can be extended with additional qualitative and statistical analyses of data, including for instance regressions, geographical distribution of diseases, and clustering. The software is released as open source-code and published under the GPL-3 license at https://github.com/mfalda/tsv2swm
Application of Semantics to Solve Problems in Life Sciences
Fecha de lectura de Tesis: 10 de diciembre de 2018La cantidad de información que se genera en la Web se ha incrementado en los últimos años. La mayor parte de esta información se encuentra accesible en texto, siendo el ser humano el principal usuario de la Web. Sin embargo, a pesar de todos los avances producidos en el área del procesamiento del lenguaje natural, los ordenadores tienen problemas para procesar esta información textual. En este cotexto, existen dominios de aplicación en los que se están publicando grandes cantidades de información disponible como datos estructurados como en el área de las Ciencias de la Vida. El análisis de estos datos es de vital importancia no sólo para el avance de la ciencia, sino para producir avances en el ámbito de la salud. Sin embargo, estos datos están localizados en diferentes repositorios y almacenados en diferentes formatos que hacen difícil su integración. En este contexto, el paradigma de los Datos Vinculados como una tecnología que incluye la aplicación de algunos estándares propuestos por la comunidad W3C tales como HTTP URIs, los estándares RDF y OWL. Haciendo uso de esta tecnología, se ha desarrollado esta tesis doctoral basada en cubrir los siguientes objetivos principales: 1) promover el uso de los datos vinculados por parte de la comunidad de usuarios del ámbito de las Ciencias de la Vida 2) facilitar el diseño de consultas SPARQL mediante el descubrimiento del modelo subyacente en los repositorios RDF 3) crear un entorno colaborativo que facilite el consumo de Datos Vinculados por usuarios finales, 4) desarrollar un algoritmo que, de forma automática, permita descubrir el modelo semántico en OWL de un repositorio RDF, 5) desarrollar una representación en OWL de ICD-10-CM llamada Dione que ofrezca una metodología automática para la clasificación de enfermedades de pacientes y su posterior validación haciendo uso de un razonador OWL
Composição de serviços para aplicações biomédicas
Doutoramento em Engenharia InformáticaA exigente inovação na área das aplicações biomédicas tem guiado a evolução
das tecnologias de informação nas últimas décadas. Os desafios associados a
uma gestão, integração, análise e interpretação eficientes dos dados
provenientes das mais modernas tecnologias de hardware e software
requerem um esforço concertado. Desde hardware para sequenciação de
genes a registos electrónicos de paciente, passando por pesquisa de
fármacos, a possibilidade de explorar com precisão os dados destes
ambientes é vital para a compreensão da saúde humana. Esta tese engloba a
discussão e o desenvolvimento de melhores estratégias informáticas para
ultrapassar estes desafios, principalmente no contexto da composição de
serviços, incluindo técnicas flexíveis de integração de dados, como
warehousing ou federação, e técnicas avançadas de interoperabilidade, como
serviços web ou LinkedData.
A composição de serviços é apresentada como um ideal genérico, direcionado
para a integração de dados e para a interoperabilidade de software.
Relativamente a esta última, esta investigação debruçou-se sobre o campo da
farmacovigilância, no contexto do projeto Europeu EU-ADR. As contribuições
para este projeto, um novo standard de interoperabilidade e um motor de
execução de workflows, sustentam a sucesso da EU-ADR Web Platform, uma
plataforma para realizar estudos avançados de farmacovigilância. No contexto
do projeto Europeu GEN2PHEN, esta investigação visou ultrapassar os
desafios associados à integração de dados distribuídos e heterogéneos no
campo do varíoma humano. Foi criada uma nova solução, WAVe - Web
Analyses of the Variome, que fornece uma coleção rica de dados de variação
genética através de uma interface Web inovadora e de uma API avançada. O
desenvolvimento destas estratégias evidenciou duas oportunidades claras na
área de software biomédico: melhorar o processo de implementação de
software através do recurso a técnicas de desenvolvimento rápidas e
aperfeiçoar a qualidade e disponibilidade dos dados através da adopção do
paradigma de web semântica.
A plataforma COEUS atravessa as fronteiras de integração e
interoperabilidade, fornecendo metodologias para a aquisição e tradução
flexíveis de dados, bem como uma camada de serviços interoperáveis para
explorar semanticamente os dados agregados. Combinando as técnicas de
desenvolvimento rápidas com a riqueza da perspectiva "Semantic Web in a
box", a plataforma COEUS é uma aproximação pioneira, permitindo o
desenvolvimento da próxima geração de aplicações biomédicas.The demand for innovation in the biomedical software domain has been an
information technologies evolution driver over the last decades. The challenges
associated with the effective management, integration, analyses and
interpretation of the wealth of life sciences information stemming from modern
hardware and software technologies require concerted efforts. From gene
sequencing hardware to pharmacology research up to patient electronic health
records, the ability to accurately explore data from these environments is vital
to further improve our understanding of human health. This thesis encloses the
discussion on building better informatics strategies to address these
challenges, primarily in the context of service composition, including
warehousing and federation strategies for resource integration, as well as web
services or LinkedData for software interoperability.
Service composition is introduced as a general principle, geared towards data
integration and software interoperability. Concerning the latter, this research
covers the service composition requirements within the pharmacovigilance
field, namely on the European EU-ADR project. The contributions to this area,
the definition of a new interoperability standard and the creation of a new
workflow-wrapping engine, are behind the successful construction of the EUADR
Web Platform, a workspace for delivering advanced pharmacovigilance
studies. In the context of the European GEN2PHEN project, this research
tackles the challenges associated with the integration of heterogeneous and
distributed data in the human variome field. For this matter, a new lightweight
solution was created: WAVe, Web Analysis of the Variome, provides a rich
collection of genetic variation data through an innovative portal and an
advanced API. The development of the strategies underlying these products
highlighted clear opportunities in the biomedical software field: enhancing the
software implementation process with rapid application development
approaches and improving the quality and availability of data with the adoption
of the Semantic Web paradigm.
COEUS crosses the boundaries of integration and interoperability as it provides
a framework for the flexible acquisition and translation of data into a semantic
knowledge base, as well as a comprehensive set of interoperability services,
from REST to LinkedData, to fully exploit gathered data semantically. By
combining the lightness of rapid application development strategies with the
richness of its "Semantic Web in a box" approach, COEUS is a pioneering
framework to enhance the development of the next generation of biomedical
applications
Recuperação de informação multimodal em repositórios de imagem médica
The proliferation of digital medical imaging modalities in hospitals and other
diagnostic facilities has created huge repositories of valuable data, often
not fully explored. Moreover, the past few years show a growing trend
of data production. As such, studying new ways to index, process and
retrieve medical images becomes an important subject to be addressed by
the wider community of radiologists, scientists and engineers. Content-based
image retrieval, which encompasses various methods, can exploit the visual
information of a medical imaging archive, and is known to be beneficial to
practitioners and researchers. However, the integration of the latest systems
for medical image retrieval into clinical workflows is still rare, and their
effectiveness still show room for improvement.
This thesis proposes solutions and methods for multimodal information
retrieval, in the context of medical imaging repositories. The major
contributions are a search engine for medical imaging studies supporting
multimodal queries in an extensible archive; a framework for automated
labeling of medical images for content discovery; and an assessment and
proposal of feature learning techniques for concept detection from medical
images, exhibiting greater potential than feature extraction algorithms that
were pertinently used in similar tasks. These contributions, each in their
own dimension, seek to narrow the scientific and technical gap towards
the development and adoption of novel multimodal medical image retrieval
systems, to ultimately become part of the workflows of medical practitioners,
teachers, and researchers in healthcare.A proliferação de modalidades de imagem médica digital, em hospitais,
clínicas e outros centros de diagnóstico, levou à criação de enormes
repositórios de dados, frequentemente não explorados na sua totalidade.
Além disso, os últimos anos revelam, claramente, uma tendência para o
crescimento da produção de dados. Portanto, torna-se importante estudar
novas maneiras de indexar, processar e recuperar imagens médicas, por
parte da comunidade alargada de radiologistas, cientistas e engenheiros. A
recuperação de imagens baseada em conteúdo, que envolve uma grande
variedade de métodos, permite a exploração da informação visual num
arquivo de imagem médica, o que traz benefícios para os médicos e
investigadores. Contudo, a integração destas soluções nos fluxos de trabalho
é ainda rara e a eficácia dos mais recentes sistemas de recuperação de
imagem médica pode ser melhorada.
A presente tese propõe soluções e métodos para recuperação de informação
multimodal, no contexto de repositórios de imagem médica. As contribuições
principais são as seguintes: um motor de pesquisa para estudos de imagem
médica com suporte a pesquisas multimodais num arquivo extensível; uma
estrutura para a anotação automática de imagens; e uma avaliação e
proposta de técnicas de representation learning para deteção automática de
conceitos em imagens médicas, exibindo maior potencial do que as técnicas
de extração de features visuais outrora pertinentes em tarefas semelhantes.
Estas contribuições procuram reduzir as dificuldades técnicas e científicas
para o desenvolvimento e adoção de sistemas modernos de recuperação de
imagem médica multimodal, de modo a que estes façam finalmente parte
das ferramentas típicas dos profissionais, professores e investigadores da área
da saúde.Programa Doutoral em Informátic
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