e-Science, caGrid, and Translational Biomedical Research

Saltz, Joel; Kurç, Tahsin; Hastings, Shannon; Langella, Stephen; Oster, Scott; Ervin, David; Sharma, Ashish; Pan, Tony; Gürcan, Metin N.; Permar, Justin; Ferreira, Renato; Payne, Philip R. O.; Çatalyürek, Ümit V.; Caserta, E.; Leone, Giovanni; Ostrowski, Michael C.; Madduri, Ravi; Foster, Ian; Madhavan, Subha

doi:10.1109/mc.2008.459

Cited by 25 publications

(27 citation statements)

References 9 publications

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“…The cancer Biomedical Informatics Grid (caBIG®) is an NIH/NCI funded project that connects researchers, physicians, and patients in the cancer research community 56 . caGrid provides the underlying infrastructure for accessing and integrating data and analytical tools deployed at different institutions within the caBIG® environment 57 ). In order to be enabled as a part of the grid, a service must comply with caGrid standards; this includes rigorous testing and mapping of API elements to the NCI Thesaurus.…”

Section: Development Of Digital Model Repository (Dmr)mentioning

confidence: 99%

Accelerating cancer systems biology research through Semantic Web technology

Wang

Sagotsky

Taylor

et al. 2012

WIREs Mechanisms of Disease

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Cancer systems biology is an interdisciplinary, rapidly expanding research field in which collaborations are a critical means to advance the field. Yet the prevalent database technologies often isolate data rather than making it easily accessible. The Semantic Web has the potential to help facilitate web-based collaborative cancer research by presenting data in a manner that is self-descriptive, human and machine readable, and easily sharable. We have created a semantically linked online Digital Model Repository (DMR) for storing, managing, executing, annotating, and sharing computational cancer models. Within the DMR, distributed, multidisciplinary, and inter-organizational teams can collaborate on projects, without forfeiting intellectual property. This is achieved by the introduction of a new stakeholder to the collaboration workflow, the institutional licensing officer, part of the Technology Transfer Office. Furthermore, the DMR has achieved silver level compatibility with the National Cancer Institute’s caBIG®, so users can not only interact with the DMR through a web browser but also through a semantically annotated and secure web service. We also discuss the technology behind the DMR leveraging the Semantic Web, ontologies, and grid computing to provide secure inter-institutional collaboration on cancer modeling projects, online grid-based execution of shared models, and the collaboration workflow protecting researchers’ intellectual property.

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Section: Development Of Digital Model Repository (Dmr)mentioning

confidence: 99%

Accelerating cancer systems biology research through Semantic Web technology

Wang

Sagotsky

Taylor

et al. 2012

WIREs Mechanisms of Disease

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“…The National Cancer Institute’s caBIG program has made significant progress in this area through the use of an architecture paradigm wherein individual datasets are kept under institutional control yet made discoverable (16, 17). A remote user can query multiple datasets across institutions.…”

Section: Discoverable Databasesmentioning

confidence: 99%

Improving Normal Tissue Complication Probability Models: The Need to Adopt a “Data-Pooling” Culture

Deasy

Bentzen

Jackson

et al. 2010

International Journal of Radiation Oncology*Biology*Physics

108

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Clinical studies of the dependence of normal tissue response on dose-volume factors are often confusingly inconsistent, as the QUANTEC reviews demonstrate. A key opportunity to accelerate progress is to begin storing high-quality datasets in repositories. Using available technology, multiple repositories could be conveniently queried, without divulging protected health information, to identify relevant sources of data for further analysis. After obtaining institutional approvals, data could then be pooled, greatly enhancing the capability to construct predictive models that are more widely applicable and better powered to accurately identify key predictive factors (whether dosimetric, image-based, clinical, socioeconomic, or biological). Data pooling has already been carried out effectively in a few normal tissue complication probability studies and should become a common strategy. KeywordsNTCP; Normal tissue complication probability models; Data sharing; Data reuse; Data pooling THE CURRENT PACE OF PROGRESS IN NORMAL TISSUE COMPLICATION PROBABILITY MODELING: STEADY, BUT SLOWProgress in radiation oncology should accelerate with an improved understanding of how treatment decisions affect outcomes. Key elements to improving predictive models of normal tissue toxicity include: discovering new factors (e.g., dosimetric, anatomic, biological) that influence risk and validating evolving models with increasingly comprehensive datasets. In

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“…Nevertheless, research projects targeting similar problems employ common principles and processes. These principles and processes can be classified into broad groups of common patterns, referred to here as pattern templates (Saltz et al, 2008a, b, c). The concept of pattern templates is inspired by the work on pattern languages (Alexander, 1977) that capture common aspects of architectural design patterns and by the principles of software design patterns for software development (Gamma et al, 1994).…”

Section: Example Integrative Biomedical Research Patternsmentioning

confidence: 99%

“…We describe these requirements using example research pattern templates (Saltz et al, 2008a, b, c). We discuss how Grid and high-performance computing techniques and middleware components could be used to address some of these requirements.…”

Section: Introductionmentioning

confidence: 99%

HPC and Grid Computing for Integrative Biomedical Research

Kurç

Hastings

Kumar

et al. 2009

The International Journal of High Performance Computing Applica

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Integrative biomedical research projects query, analyze, and integrate many different data types and make use of datasets obtained from measurements or simulations of structure and function at multiple biological scales. With the increasing availability of high-throughput and high-resolution instruments, the integrative biomedical research imposes many challenging requirements on software middleware systems. In this paper, we look at some of these requirements using example research pattern templates. We then discuss how middleware systems, which incorporate Grid and high-performance computing, could be employed to address the requirements.

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e-Science, caGrid, and Translational Biomedical Research

Cited by 25 publications

References 9 publications

Accelerating cancer systems biology research through Semantic Web technology

Accelerating cancer systems biology research through Semantic Web technology

Improving Normal Tissue Complication Probability Models: The Need to Adopt a “Data-Pooling” Culture

HPC and Grid Computing for Integrative Biomedical Research

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