GridR: An R-Based Grid-Enabled Tool for Data Analysis in ACGT Clinico-Genomics Trials

Wegener, Dennis; Sengstag, Thierry; Sfakianakis, Stelios; Rüping, Stefan; Assi, Anthony

doi:10.1109/e-science.2007.41

Cited by 18 publications

(10 citation statements)

References 15 publications

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“…Knowledge discovery in clinico-genomic data presents a new array of challenges since it differs significantly from the original problems of data analysis that prompted the development of grid technologies, for example in particle physics and astronomy [9,34]. The exploitation of semantics information in the description of data sources and data analysis tools is of high importance for the effective design and realization of knowledge discovery processes.…”

Section: Knowledge Discovery Servicesmentioning

confidence: 99%

Developing a European grid infrastructure for cancer research: vision, architecture, and services

Tsiknakis¹

2015

ecancer

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Life sciences are currently at the centre of an information revolution. The nature and amount of information now available opens up areas of research that were once in the realm of science fiction. During this information revolution, the data-gathering capabilities have greatly surpassed the data-analysis techniques. Data integration across heterogeneous data sources and data aggregation across different aspects of the biomedical spectrum, therefore, is at the centre of current biomedical and pharmaceutical R&D. This paper reports on original results from the ACGT integrated project, focusing on the design and development of a European Biomedical Grid infrastructure in support of multi-centric, post-genomic clinical trials (CTs) on cancer. Post-genomic CTs use multi-level clinical and genomic data and advanced computational analysis and visualization tools to test hypotheses in trying to identify the molecular reasons for a disease and the stratification of patients in terms of treatment.The paper provides a presentation of the needs of users involved in post-genomic CTs and presents indicative scenarios, which drive the requirements of the engineering phase of the project. Subsequently, the initial architecture specified by the project is presented, and its services are classified and discussed. A range of such key services, including the Master Ontology on sCancer, which lie at the heart of the integration architecture of the project, is presented. Special efforts have been taken to describe the methodological and technological framework of the project, enabling the creation of a legally compliant and trustworthy infrastructure. Finally, a short discussion of the forthcoming work is included, and the potential involvement of the cancer research community in further development or utilization of the infrastructure is described.

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Section: Knowledge Discovery Servicesmentioning

confidence: 99%

Developing a European grid infrastructure for cancer research: vision, architecture, and services

Tsiknakis¹

2015

ecancer

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“…The ACGT Workflow Editor [27], [28] is the end user application for designing and executing high-level scientific workflows (see Fig. 7).…”

Section: Executing the Oncosimulator Through Workflowsmentioning

confidence: 99%

The Technologically Integrated Oncosimulator: Combining Multiscale Cancer Modeling With Information Technology in the In Silico Oncology Context

Stamatakos

Dionysiou

Lunzer

et al. 2014

IEEE J. Biomed. Health Inform.

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This paper outlines the major components and function of the technologically integrated oncosimulator developed primarily within the Advancing Clinico Genomic Trials on Cancer (ACGT) project. The Oncosimulator is defined as an information technology system simulating in vivo tumor response to therapeutic modalities within the clinical trial context. Chemotherapy in the neoadjuvant setting, according to two real clinical trials concerning nephroblastoma and breast cancer, has been considered. The spatiotemporal simulation module embedded in the Oncosimulator is based on the multiscale, predominantly top-down, discrete entity-discrete event cancer simulation technique developed by the In Silico Oncology Group, National Technical University of Athens. The technology modules include multiscale data handling, image processing, invocation of code execution via a spreadsheet-inspired environment portal, execution of the code on the grid, and the visualization of the predictions. A refining scenario for the eventual coupling of the oncosimulator with immunological models is also presented. Parameter values have been adapted to multiscale clinical trial data in a consistent way, thus supporting the predictive potential of the oncosimulator. Indicative results demonstrating various aspects of the clinical adaptation and validation process are presented. Completion of these processes is expected to pave the way for the clinical translation of the system.

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“…The need for easier methods was soon quite evident and other packages like SNOW [13] were introduced to mitigate the complexity of development and the accessibility to remote resources. In the context of grid computing the package gridR [14] is the first main contribution. It submits R functions in a grid environment using Condor [15], Globus [16], or single servers.…”

Section: B Related Work and Open Issuesmentioning

confidence: 99%

Integration of Heterogeneous and Non-dedicated Environments for R

Vera¹,

Suppi²

2010

2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing

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Parallel computing is becoming essential for nowadays data analysis in several disciplines. In order to profit from parallel processing of experimental data, specialized skills, software tools and suitable computing resources are required. Desktop grids and volunteer-based systems have proved themselves as powerful options where distributed idle resources from heterogeneous computers are aggregated to build powerful metacomputers. Software solutions are required to automate and assist the process of transformation and adaptation of current and new applications to run in these environments. Finally, it is desirable, for the same tool, to provide an efficient solution to orchestrate the execution of these programs using a diversity of dynamic environments. In this paper we describe an implementation of an integrated solution for the R language which allows the transformation and execution of parallel loops in heterogeneous and nondedicated environments. The results obtained allow us to prove the feasibility of our proposal. Furthermore, several issues that tools like this must consider to improve their performance when integrating heterogeneous systems are described.

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GridR: An R-Based Grid-Enabled Tool for Data Analysis in ACGT Clinico-Genomics Trials

Cited by 18 publications

References 15 publications

Developing a European grid infrastructure for cancer research: vision, architecture, and services

Developing a European grid infrastructure for cancer research: vision, architecture, and services

The Technologically Integrated Oncosimulator: Combining Multiscale Cancer Modeling With Information Technology in the In Silico Oncology Context

Integration of Heterogeneous and Non-dedicated Environments for R

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