Concept: Web application framework
BACKGROUND: U-Compare is a text mining platform that allows the construction, evaluation and comparison of text miningworkflows. U-Compare contains a large library of components that are tuned to the biomedical domain. Userscan rapidly develop biomedical text mining workflows by mixing and matching U-Compare’s components.Workflows developed using U-Compare can be exported and sent to other users who, in turn, can import andre-use them. However, the resulting workflows are standalone applications, i.e., software tools that run and areaccessible only via a local machine, and that can only be run with the U-Compare platform. RESULTS: We address the above issues by extending U-Compare to convert standalone workflows into web servicesautomatically, via a two-click process. The resulting web services can be registered on a central server andmade publicly available. Alternatively, users can make web services available on their own servers, afterinstalling the web application framework, which is part of the extension to U-Compare. We have performed auser-oriented evaluation of the proposed extension, by asking users who have tested the enhanced functionalityof U-Compare to complete questionnaires that assess its functionality, reliability, usability, efficiency andmaintainability. The results obtained reveal that the new functionality is well received by users. CONCLUSIONS: The web services produced by U-Compare are built on top of open standards, i.e., REST and SOAP protocols,and therefore, they are decoupled from the underlying platform. Exported workflows can be integrated withany application that supports these open standards. We demonstrate how the newly extended U-Compareenhances the cross-platform interoperability of workflows, by seamlessly importing a number of text miningworkflow web services exported from U-Compare into Taverna, i.e., a generic scientific workflow constructionplatform.
A web-based application is developed to generate 4D-QSAR descriptors using the LQTA-QSAR methodology, based on molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package. The LQTAGrid module calculates the intermolecular interaction energies at each grid point, considering probes and all aligned conformations resulting from MD simulations. These interaction energies are the independent variables or descriptors employed in a QSAR analysis. A friendly front end web interface, built using the Django framework and Python programming language, integrates all steps of the LQTA-QSAR methodology in a way that is transparent to the user, and in the backend, GROMACS and LQTAGrid are executed to generate 4D-QSAR descriptors to be used later in the process of QSAR model building.
Overcoming Addictions (OA) is an abstinence-oriented, cognitive behavioral, Web application based on the program of SMART Recovery. SMART Recovery is an organization that has adapted empirically supported treatment strategies for use in a mutual help framework with in-person meetings, online meetings, a forum, and other resources.
The HTSstation analysis portal is a suite of simple web forms coupled to modular analysis pipelines for various applications of High-Throughput Sequencing including ChIP-seq, RNA-seq, 4C-seq and re-sequencing. HTSstation offers biologists the possibility to rapidly investigate their HTS data using an intuitive web application with heuristically pre-defined parameters. A number of open-source software components have been implemented and can be used to build, configure and run HTS analysis pipelines reactively. Besides, our programming framework empowers developers with the possibility to design their own workflows and integrate additional third-party software. The HTSstation web application is accessible at http://htsstation.epfl.ch.
The study of human perception has helped psychologists effectively communicate data rich stories by converting numbers into graphical illustrations and data visualization remains a powerful means for psychology to discover, understand, and present results to others. However, despite an exponential rise in computing power, the World Wide Web, and ever more complex data sets, psychologists often limit themselves to static visualizations. While these are often adequate, their application across professional psychology remains limited. This is surprising as it is now possible to build dynamic representations based around simple or complex psychological data sets. Previously, knowledge of HTML, CSS, or Java was essential, but here we develop several interactive visualizations using a simple web application framework that runs under the R statistical platform: Shiny. Shiny can help researchers quickly produce interactive data visualizations that will supplement and support current and future publications. This has clear benefits for researchers, the wider academic community, students, practitioners, and interested members of the public.
Harvest: an open platform for developing web-based biomedical data discovery and reporting applications
- Journal of the American Medical Informatics Association : JAMIA
- Published almost 6 years ago
Biomedical researchers share a common challenge of making complex data understandable and accessible as they seek inherent relationships between attributes in disparate data types. Data discovery in this context is limited by a lack of query systems that efficiently show relationships between individual variables, but without the need to navigate underlying data models. We have addressed this need by developing Harvest, an open-source framework of modular components, and using it for the rapid development and deployment of custom data discovery software applications. Harvest incorporates visualizations of highly dimensional data in a web-based interface that promotes rapid exploration and export of any type of biomedical information, without exposing researchers to underlying data models. We evaluated Harvest with two cases: clinical data from pediatric cardiology and demonstration data from the OpenMRS project. Harvest’s architecture and public open-source code offer a set of rapid application development tools to build data discovery applications for domain-specific biomedical data repositories. All resources, including the OpenMRS demonstration, can be found at http://harvest.research.chop.edu.
While textual analysis of the journal literature is a burgeoning field, there is still a profound lack of user-friendly software for accomplishing this task. RLetters is a free, open-source web application which provides researchers with an environment in which they can select sets of journal articles and analyze them with cutting-edge textual analysis tools. RLetters allows users without prior expertise in textual analysis to analyze word frequency, collocations, cooccurrences, term networks, and more. It is implemented in Ruby and scripts are provided to automate deployment.
We present the postmodification of a diamondoid 3D supramolecular organic framework (SOF) to append [Ru(BPY)3]2+ groups through the formation of a hydrazone bond. The resulting SOF works as an efficient recyclable heterogeneous catalyst for visible-light-induced reduction of aromatic azides to amines.
The drug-gene interaction database (DGIdb, www.dgidb.org) consolidates, organizes and presents drug-gene interactions and gene druggability information from papers, databases and web resources. DGIdb normalizes content from 30 disparate sources and allows for user-friendly advanced browsing, searching and filtering for ease of access through an intuitive web user interface, application programming interface (API) and public cloud-based server image. DGIdb v3.0 represents a major update of the database. Nine of the previously included 24 sources were updated. Six new resources were added, bringing the total number of sources to 30. These updates and additions of sources have cumulatively resulted in 56 309 interaction claims. This has also substantially expanded the comprehensive catalogue of druggable genes and anti-neoplastic drug-gene interactions included in the DGIdb. Along with these content updates, v3.0 has received a major overhaul of its codebase, including an updated user interface, preset interaction search filters, consolidation of interaction information into interaction groups, greatly improved search response times and upgrading the underlying web application framework. In addition, the expanded API features new endpoints which allow users to extract more detailed information about queried drugs, genes and drug-gene interactions, including listings of PubMed IDs, interaction type and other interaction metadata.
DAME (Dynamic Assessment of Microbial Ecology) is a Shiny-based web application for interactive analysis and visualization of microbial sequencing data. DAME provides researchers not familiar with R programming the ability to access the most current R functions utilized for ecology and gene sequencing data analyses. Currently, DAME supports group comparisons of several ecological estimates of α-diversity and β-diversity, along with differential abundance analysis of individual taxa. Using the Shiny framework, the user has complete control of all aspects of the data analysis, including sample/experimental group selection and filtering, estimate selection, statistical methods, and visualization parameters. Furthermore, graphical and tabular outputs are supported by R packages using D3.js and are fully interactive.