caGrid is the core Grid architecture of the NCI-sponsored cancer Biomedical Informatics Grid (caBIG) program. The current release, caGrid version 1.0, is developed as the production Grid software infrastructure of caBIG. Based on feedback from adopters of the previous version (caGrid 0.5), it has been significantly enhanced with new features and improvements to existing components. This paper presents an overview of caGrid 1.0, its main components, and enhancements over caGrid 0.5.
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AMIA Jt Summits Transl Sci Proc
December 2013
Department of Biomedical Informatics, University of Utah, Salt Lake City, UT.
As the biomedical community collects and generates more and more data, the need to describe these datasets for exchange and interoperability becomes crucial. This paper presents a mapping algorithm that can help developers expose local implementations described with UML through standard terminologies. The input UML class or attribute name is first normalized and tokenized, then lookups in a UMLS-based dictionary are performed.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
September 2013
Institute of Computer Science at FORTH, Vassilika Vouton, GR-70013 Heraklion, Crete, Greece.
The TUMOR project aims at developing a European clinically oriented semantic-layered cancer digital model repository from existing EU projects that will be interoperable with the US grid-enabled semantic-layered digital model repository platform at CViT.org (Center for the Development of a Virtual Tumor, Massachusetts General Hospital (MGH), Boston, USA) which is NIH/NCI-caGRID compatible. In this paper we describe the modular and federated architecture of TUMOR that effectively addresses model integration, interoperability, and security related issues.
View Article and Find Full Text PDFStud Health Technol Inform
September 2012
Center for High Performance Computing, University of Utah, Salt Lake City, UT, USA.
This paper presents a study of the performance of federated queries implemented in a system that simulates the architecture proposed for the Scalable Architecture for Federated Translational Inquiries Network (SAFTINet). Performance tests were conducted using both physical hardware and virtual machines within the test laboratory of the Center for High Performance Computing at the University of Utah. Tests were performed on SAFTINet networks ranging from 4 to 32 nodes with databases containing synthetic data for several million patients.
View Article and Find Full Text PDFBackground: Personalised medicine provides patients with treatments that are specific to their genetic profiles. It requires efficient data sharing of disparate data types across a variety of scientific disciplines, such as molecular biology, pathology, radiology and clinical practice. Personalised medicine aims to offer the safest and most effective therapeutic strategy based on the gene variations of each subject.
View Article and Find Full Text PDFMethods Inf Med
September 2012
Information Warehouse, The Ohio State University Medical Center, Columbus, Ohio, USA.
Objective: To qualify the use of patient clinical records as non-human-subject for research purpose, electronic medical record data must be de-identified so there is minimum risk to protected health information exposure. This study demonstrated a robust framework for structured data de-identification that can be applied to any relational data source that needs to be de-identified.
Methods: Using a real world clinical data warehouse, a pilot implementation of limited subject areas were used to demonstrate and evaluate this new de-identification process.
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