Informatics in radiology: An open-source and open-access cancer biomedical informatics grid annotation and image markup template builder.
Ontology highlight
ABSTRACT:
SUBMITTER: Mongkolwat P
PROVIDER: S-EPMC3393884 | biostudies-other | 2012 Jul-Aug
REPOSITORIES: biostudies-other
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Project description:Background. Open access (OA) journals allows access to research papers free of charge to the reader. Traditionally, biomedical researchers use databases like MEDLINE and EMBASE to discover new advances. However, biomedical OA journals might not fulfill such databases' criteria, hindering dissemination. The Directory of Open Access Journals (DOAJ) is a database exclusively listing OA journals. The aim of this study was to investigate DOAJ's coverage of biomedical OA journals compared with the conventional biomedical databases. Methods. Information on all journals listed in four conventional biomedical databases (MEDLINE, PubMed Central, EMBASE and SCOPUS) and DOAJ were gathered. Journals were included if they were (1) actively publishing, (2) full OA, (3) prospectively indexed in one or more database, and (4) of biomedical subject. Impact factor and journal language were also collected. DOAJ was compared with conventional databases regarding the proportion of journals covered, along with their impact factor and publishing language. The proportion of journals with articles indexed by DOAJ was determined. Results. In total, 3,236 biomedical OA journals were included in the study. Of the included journals, 86.7% were listed in DOAJ. Combined, the conventional biomedical databases listed 75.0% of the journals; 18.7% in MEDLINE; 36.5% in PubMed Central; 51.5% in SCOPUS and 50.6% in EMBASE. Of the journals in DOAJ, 88.7% published in English and 20.6% had received impact factor for 2012 compared with 93.5% and 26.0%, respectively, for journals in the conventional biomedical databases. A subset of 51.1% and 48.5% of the journals in DOAJ had articles indexed from 2012 and 2013, respectively. Of journals exclusively listed in DOAJ, one journal had received an impact factor for 2012, and 59.6% of the journals had no content from 2013 indexed in DOAJ. Conclusions. DOAJ is the most complete registry of biomedical OA journals compared with five conventional biomedical databases. However, DOAJ only indexes articles for half of the biomedical journals listed, making it an incomplete source for biomedical research papers in general.
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Project description:Passive acoustic monitoring is used widely in ecology, biodiversity, and conservation studies. Data sets collected via acoustic monitoring are often extremely large and built to be processed automatically using artificial intelligence and machine learning models, which aim to replicate the work of domain experts. These models, being supervised learning algorithms, need to be trained on high quality annotations produced by experts. Since the experts are often resource-limited, a cost-effective process for annotating audio is needed to get maximal use out of the data. We present an open-source interactive audio data annotation tool, NEAL (Nature+Energy Audio Labeller). Built using R and the associated Shiny framework, the tool provides a reactive environment where users can quickly annotate audio files and adjust settings that automatically change the corresponding elements of the user interface. The app has been designed with the goal of having both expert birders and citizen scientists contribute to acoustic annotation projects. The popularity and flexibility of R programming in bioacoustics means that the Shiny app can be modified for other bird labelling data sets, or even to generic audio labelling tasks. We demonstrate the app by labelling data collected from wind farm sites across Ireland.
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Project description:Observational and experimental studies of rodent voiding behaviors have greatly contributed to our understanding of lower urinary tract function including the complex social, environmental, and internal stimuli that affect voiding in health and models of disease. Void spot assays (VSA), cystometry (awake or anesthetized), and uroflowmetry are techniques commonly used in rodent models to assess voiding. Uroflowmetry is non-invasive and can be performed multiple times in the same freely moving animals and can be used to generate synchronized video corresponding to each void to characterize micturition patterns (e.g., droplets versus solid stream). However, approaches to evaluate uroflowmetry in rodent models vary widely across laboratories. Most importantly, an open access software to run these tests is not freely available (although complete systems are commercially available), limiting use of this important assay. We developed the Void Sorcerer, an uroflowmetry system for mice for reliable determination of frequency, voided volume, voiding duration, interval times between micturitions, and flow rate. This report provides a detailed description of how to build this system and includes open access software for developing uroflowmetry capability in their laboratories and improve upon it in a cost-effective manner. Our goals are to improve access, increase reproducibility among laboratories, and facilitate standardizing testing procedures.
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Project description:OBJECTIVE:Finding relevant datasets is important for promoting data reuse in the biomedical domain, but it is challenging given the volume and complexity of biomedical data. Here we describe the development of an open source biomedical data discovery system called DataMed, with the goal of promoting the building of additional data indexes in the biomedical domain. MATERIALS AND METHODS:DataMed, which can efficiently index and search diverse types of biomedical datasets across repositories, is developed through the National Institutes of Health-funded biomedical and healthCAre Data Discovery Index Ecosystem (bioCADDIE) consortium. It consists of 2 main components: (1) a data ingestion pipeline that collects and transforms original metadata information to a unified metadata model, called DatA Tag Suite (DATS), and (2) a search engine that finds relevant datasets based on user-entered queries. In addition to describing its architecture and techniques, we evaluated individual components within DataMed, including the accuracy of the ingestion pipeline, the prevalence of the DATS model across repositories, and the overall performance of the dataset retrieval engine. RESULTS AND CONCLUSION:Our manual review shows that the ingestion pipeline could achieve an accuracy of 90% and core elements of DATS had varied frequency across repositories. On a manually curated benchmark dataset, the DataMed search engine achieved an inferred average precision of 0.2033 and a precision at 10 (P@10, the number of relevant results in the top 10 search results) of 0.6022, by implementing advanced natural language processing and terminology services. Currently, we have made the DataMed system publically available as an open source package for the biomedical community.
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Project description:BackgroundThe detection of pathogens in complex sample backgrounds has been revolutionized by wide access to next-generation sequencing (NGS) platforms. However, analytical methods to support NGS platforms are not as uniformly available. Pathosphere (found at Pathosphere.org) is a cloud - based open - sourced community tool that allows for communication, collaboration and sharing of NGS analytical tools and data amongst scientists working in academia, industry and government. The architecture allows for users to upload data and run available bioinformatics pipelines without the need for onsite processing hardware or technical support.ResultsThe pathogen detection capabilities hosted on Pathosphere were tested by analyzing pathogen-containing samples sequenced by NGS with both spiked human samples as well as human and zoonotic host backgrounds. Pathosphere analytical pipelines developed by Edgewood Chemical Biological Center (ECBC) identified spiked pathogens within a common sample analyzed by 454, Ion Torrent, and Illumina sequencing platforms. ECBC pipelines also correctly identified pathogens in human samples containing arenavirus in addition to animal samples containing flavivirus and coronavirus. These analytical methods were limited in the detection of sequences with limited homology to previous annotations within NCBI databases, such as parvovirus. Utilizing the pipeline-hosting adaptability of Pathosphere, the analytical suite was supplemented by analytical pipelines designed by the United States Army Medical Research Insititute of Infectious Diseases and Walter Reed Army Institute of Research (USAMRIID-WRAIR). These pipelines were implemented and detected parvovirus sequence in the sample that the ECBC iterative analysis previously failed to identify.ConclusionsBy accurately detecting pathogens in a variety of samples, this work demonstrates the utility of Pathosphere and provides a platform for utilizing, modifying and creating pipelines for a variety of NGS technologies developed to detect pathogens in complex sample backgrounds. These results serve as an exhibition for the existing pipelines and web-based interface of Pathosphere as well as the plug-in adaptability that allows for integration of newer NGS analytical software as it becomes available.
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Project description:We present the Molecular Software Library (MSL), a C++ library for molecular modeling. MSL is a set of tools that supports a large variety of algorithms for the design, modeling, and analysis of macromolecules. Among the main features supported by the library are methods for applying geometric transformations and alignments, the implementation of a rich set of energy functions, side chain optimization, backbone manipulation, calculation of solvent accessible surface area, and other tools. MSL has a number of unique features, such as the ability of storing alternative atomic coordinates (for modeling) and multiple amino acid identities at the same backbone position (for design). It has a straightforward mechanism for extending its energy functions and can work with any type of molecules. Although the code base is large, MSL was created with ease of developing in mind. It allows the rapid implementation of simple tasks while fully supporting the creation of complex applications. Some of the potentialities of the software are demonstrated here with examples that show how to program complex and essential modeling tasks with few lines of code. MSL is an ongoing and evolving project, with new features and improvements being introduced regularly, but it is mature and suitable for production and has been used in numerous protein modeling and design projects. MSL is open-source software, freely downloadable at http://msl-libraries.org. We propose it as a common platform for the development of new molecular algorithms and to promote the distribution, sharing, and reutilization of computational methods.
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Project description:The localization of intracranial electrodes is a fundamental step in the analysis of invasive electroencephalography (EEG) recordings in research and clinical practice. The conclusions reached from the analysis of these recordings rely on the accuracy of electrode localization in relationship to brain anatomy. However, currently available techniques for localizing electrodes from magnetic resonance (MR) and/or computerized tomography (CT) images are time consuming and/or limited to particular electrode types or shapes. Here we present iElectrodes, an open-source toolbox that provides robust and accurate semi-automatic localization of both subdural grids and depth electrodes. Using pre- and post-implantation images, the method takes 2-3 min to localize the coordinates in each electrode array and automatically number the electrodes. The proposed pre-processing pipeline allows one to work in a normalized space and to automatically obtain anatomical labels of the localized electrodes without neuroimaging experts. We validated the method with data from 22 patients implanted with a total of 1,242 electrodes. We show that localization distances were within 0.56 mm of those achieved by experienced manual evaluators. iElectrodes provided additional advantages in terms of robustness (even with severe perioperative cerebral distortions), speed (less than half the operator time compared to expert manual localization), simplicity, utility across multiple electrode types (surface and depth electrodes) and all brain regions.
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Project description:The Cancer Biomedical Informatics Grid (caBIG) is a network of individuals and institutions, creating a world wide web of cancer research. An important aspect of this informatics effort is the development of consistent practices for data standards development, using a multi-tier approach that facilitates semantic interoperability of systems. The semantic tiers include (1) information models, (2) common data elements, and (3) controlled terminologies and ontologies. The College of American Pathologists (CAP) cancer protocols and checklists are an important reporting standard in pathology, for which no complete electronic data standard is currently available.In this manuscript, we provide a case study of Cancer Common Ontologic Representation Environment (caCORE) data standard implementation of the CAP cancer protocols and checklists model--an existing and complex paper based standard. We illustrate the basic principles, goals and methodology for developing caBIG models.Using this example, we describe the process required to develop the model, the technologies and data standards on which the process and models are based, and the results of the modeling effort. We address difficulties we encountered and modifications to caCORE that will address these problems. In addition, we describe four ongoing development projects that will use the emerging CAP data standards to achieve integration of tissue banking and laboratory information systems.The CAP cancer checklists can be used as the basis for an electronic data standard in pathology using the caBIG semantic modeling methodology.
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Project description:Cryo-electron tomography (cryo-ET) enables molecular-resolution 3D imaging of complex biological specimens such as viral particles, cellular sections, and in some cases, whole cells. This enables the structural characterization of molecules in their near-native environments, without the need for purification or separation, thereby preserving biological information such as conformational states and spatial relationships between different molecular species. Subtomogram averaging is an image processing workflow that allows users to leverage cryo-ET data to identify and localize target molecules, determine high-resolution structures of repeating molecular species, and classifying different conformational states. Here we describe STOPGAP, an open-source package for subtomogram averaging designed to provide users with fine control over each of these steps. In providing detailed descriptions of the image processing algorithms that STOPGAP uses, we intend for this manuscript to also serve as a technical resource to users as well as further community-driven software development.
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Project description:BackgroundExamining whether disease cases are clustered in space is an important part of epidemiological research. Another important part of spatial epidemiology is testing whether patients suffering from a disease are more, or less, exposed to environmental factors of interest than adequately defined controls. Both approaches involve determining the number of cases and controls (or population at risk) in specific zones. For cluster searches, this often must be done for millions of different zones. Doing this by calculating distances can lead to very lengthy computations. In this work we discuss the computational advantages of geographical grid-based methods, and introduce an open source software (FGBASE) which we have created for this purpose.MethodsGeographical grids based on the Lambert Azimuthal Equal Area projection are well suited for spatial epidemiology because they preserve area: each cell of the grid has the same area. We describe how data is projected onto such a grid, as well as grid-based algorithms for spatial epidemiological data-mining. The software program (FGBASE), that we have developed, implements these grid-based methods.ResultsThe grid based algorithms perform extremely fast. This is particularly the case for cluster searches. When applied to a cohort of French Type 1 Diabetes (T1D) patients, as an example, the grid based algorithms detected potential clusters in a few seconds on a modern laptop. This compares very favorably to an equivalent cluster search using distance calculations instead of a grid, which took over 4 hours on the same computer. In the case study we discovered 4 potential clusters of T1D cases near the cities of Le Havre, Dunkerque, Toulouse and Nantes. One example of environmental analysis with our software was to study whether a significant association could be found between distance to vineyards with heavy pesticide. None was found. In both examples, the software facilitates the rapid testing of hypotheses.ConclusionsGrid-based algorithms for mining spatial epidemiological data provide advantages in terms of computational complexity thus improving the speed of computations. We believe that these methods and this software tool (FGBASE) will lower the computational barriers to entry for those performing epidemiological research.
| S-EPMC4233060 | biostudies-literature