Solutions for biomedical grid computing—Case studies from the D-Grid project Services@MediGRID

Dickmann, Frank; Falkner, Jürgen; Gunia, Wilfried; Hampe, Jochen; Hausmann, Michael; Herrmann, Alexander; Kepper, Nick; Knoch, Tobias; Lauterbach, Svenja; Lippert, Jörg; Peter, Kathrin; Schmitt, Eberhard; Schwardmann, Ulrich; Solodenko, Juri; Sommerfeld, Dietmar; Steinke, Thomas; Weisbecker, Anette; Sax, Ulrich

doi:10.1016/j.jocs.2011.06.006

Cited by 9 publications

(11 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Through it we can manage and track in a seamless and efficient way both relatively small datasets, such as microarray expressions profiles, and, in a soon-to-be future, larger ones, as whole genome sequences. iRODS is already used in production in various genomic centres and biomedical consortia, such as the Wellcome Trust Sanger Institute [ 45 ] and the Services@MediGRID project [ 46 ].…”

Section: Discussionmentioning

confidence: 99%

A digital repository with an extensible data model for biobanking and genomic analysis management

et al. 2014

View full text Add to dashboard Cite

MotivationMolecular biology laboratories require extensive metadata to improve data collection and analysis. The heterogeneity of the collected metadata grows as research is evolving in to international multi-disciplinary collaborations and increasing data sharing among institutions. Single standardization is not feasible and it becomes crucial to develop digital repositories with flexible and extensible data models, as in the case of modern integrated biobanks management.ResultsWe developed a novel data model in JSON format to describe heterogeneous data in a generic biomedical science scenario. The model is built on two hierarchical entities: processes and events, roughly corresponding to research studies and analysis steps within a single study. A number of sequential events can be grouped in a process building up a hierarchical structure to track patient and sample history. Each event can produce new data. Data is described by a set of user-defined metadata, and may have one or more associated files. We integrated the model in a web based digital repository with a data grid storage to manage large data sets located in geographically distinct areas. We built a graphical interface that allows authorized users to define new data types dynamically, according to their requirements. Operators compose queries on metadata fields using a flexible search interface and run them on the database and on the grid. We applied the digital repository to the integrated management of samples, patients and medical history in the BIT-Gaslini biobank. The platform currently manages 1800 samples of over 900 patients. Microarray data from 150 analyses are stored on the grid storage and replicated on two physical resources for preservation. The system is equipped with data integration capabilities with other biobanks for worldwide information sharing.ConclusionsOur data model enables users to continuously define flexible, ad hoc, and loosely structured metadata, for information sharing in specific research projects and purposes. This approach can improve sensitively interdisciplinary research collaboration and allows to track patients' clinical records, sample management information, and genomic data. The web interface allows the operators to easily manage, query, and annotate the files, without dealing with the technicalities of the data grid.

show abstract

Section: Discussionmentioning

confidence: 99%

A digital repository with an extensible data model for biobanking and genomic analysis management

et al. 2014

View full text Add to dashboard Cite

show abstract

“…The resulting HHIEA establishes a more effective and flexible architecture that facilitates the exchange of information between multiple HIS sources and provides a comprehensive and integrated view on healthcare data. HHIEA combines: data warehouse (Prokosch & Ganslandt, 2009;Bauer & Günzel, 2013), SOA (Rosen, 2008;Ryan & Eklund, 2008), grid computing (Dickmann et al, 2012;Foster, 2002), and the publish/subscribe paradigm (Eugster et al, 2003;Singh, Vargas, Bacon, & Moody, 2008). The proposed HHIEA, as shown in Figure 7, is presented in five logical groups in separately labeled subsections, namely: the Data Layer where all of the data suppliers and data consumers are logically part of this group; ID Management that is utilized to both identify and differentiate patients and organizations participating in the HIE; HIE Management that is composed of tasks related to the maintenance of medical record references; Security that contains components that are related to the management of audit trails, patient consents, and participant authentication; and, a Health Service Bus that is responsible for ensuring the precise passing messages between the HIS sources.…”

Section: A Hybrid Hie Architecture (Hhiea)mentioning

confidence: 99%

Toward Integrating Healthcare Data and Systems

Ziminski

Demurjian

Sanzi

et al. 2016

Advances in Healthcare Information Systems and Administration

View full text Add to dashboard Cite

The adoption of health information systems and the integration of healthcare data and systems into efficient cross-institutional collaboration workflows of stakeholders (e.g., medical providers such as physicians, hospitals, clinics, labs, etc.) is a challenging problem for the healthcare domain. This chapter studies the way that well-established software engineering concepts and architectural styles can be employed to satisfy requirements of the healthcare domain and ease health information exchange (HIE) between stakeholders. Towards this goal, this chapter proposes a hybrid HIE architecture (HHIEA) that leverages the studied styles that include service-oriented architecture, grid computing, publish/subscribe paradigm, and data warehousing to allow the health information systems of stakeholders to be integrated to facilitate collaboration among medical providers. To demonstrate the feasibility and utility of the HHIEA, a realistic regional healthcare scenario is introduced that illustrates the interactions of stakeholders across an integrated collection of health information systems.

show abstract

“…The additional restraints can be incorporated into the algorithm. Examples of how this interplay between medical diagnostics, industrial probe supply, and academic research can be realised and incorporated into a business model, are described in [17].…”

Section: Problem Formulation and Data Structuresmentioning

confidence: 99%

Combinatorial selection of short triplex forming oligonucleotides for fluorescence in situ hybridisation COMBO-FISH

Schmitt

Wagner

Hausmann

2012

Journal of Computational Science

Self Cite

View full text Add to dashboard Cite

Solutions for biomedical grid computing—Case studies from the D-Grid project Services@MediGRID

Cited by 9 publications

References 64 publications

A digital repository with an extensible data model for biobanking and genomic analysis management

A digital repository with an extensible data model for biobanking and genomic analysis management

Toward Integrating Healthcare Data and Systems

Combinatorial selection of short triplex forming oligonucleotides for fluorescence in situ hybridisation COMBO-FISH

Contact Info

Product

Resources

About