Middleware Architecture Approaches for Collaborative Cancer Research

  • Tahsin Kurc
  • Ashish Sharma
  • Scott Oster
  • Tony Pan
  • Shannon Hastings
  • Stephen Langella
  • David Ervin
  • Justin Permar
  • Daniel Brat
  • T. J. Fitzgerald
  • James Purdy
  • Walter Bosch
  • Joel Saltz
Chapter

Abstract

As our ability to capture and generate large biomedical datasets improves, researchers increasingly need to synthesize information using a variety of data types, data systems, and analysis tools. The need for informatics support to facilitate coordinated and federated access to disparate data and analysis resources is more pronounced in collaborative basic, clinical, and translational research studies spanning multiple institutions. This chapter presents a high-level overview of several middleware architecture frameworks and technologies and discusses how these approaches can be employed to address the informatics requirements of large-scale and collaborative cancer research.

Keywords

Hull Berman 

Notes

Acknowledgments

This work is supported in part by the National Cancer Institute, National Institutes of Health, through the caBIG® program with Contracts 28X193/N01-CO-12400, HHSN261200800001E, 94995NBS23, and 85983CBS43; by Grant R01LM009239 from the National Library of Medicine; by Grant R24HL085343 from the National Heart, Lung, and Blood Institute; by PHS Grant UL1RR025008 from the Clinical and Translational Science Award Program, NIH; and by Grants CNS-0403342, CNS-0426241, CSR-0615412, CCF-0342615, CNS-0615155, CNS-0406386 from the National Science Foundation. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government. The In Silico Brain Tumor Research Center is a multi-institutional collaborative effort, funded by the National Cancer Institute, between Emory University (Investigators: Joel Saltz, Daniel Brat, Carlos Moreno, Erwin Van Meir, Chad Holder, Tahsin Kurc, Ashish Sharma), Henry Ford Health System (Investigator: Tom Mikkelsen), Stanford University (Investigator: Daniel Rubin), and Thomas Jefferson University (Investigator: Adam Flanders).

References

  1. Amendolia S, Brady M, Mcclatchey R et al (2003) Mammogrid: large-scale distributed mammogram analysis. Stud Health Technol Inform 95:194–199PubMedGoogle Scholar
  2. Berman F, Hey A, Fox G (eds) (2003) Grid computing: making the global infrastructure a reality. Wiley, New YorkGoogle Scholar
  3. Brady M, Gavaghan D, Simpson A et al (2003) eDiamond: a grid-enabled federated database of annotated mammograms. In: Berman F, Fox G, Hey A (eds) Grid computing: making the global infrastructure a reality. Wiley, New YorkGoogle Scholar
  4. caBIG (2009) The cancer biomedical informatics grid. Retrieved September 2009 from https://cabig.nci.nih.gov
  5. caXchange (2009) The caXchange project. From https://cabig.nci.nih.gov/tools/LabIntegrationHub
  6. Covitz P, Hartel F, Schaefer C, Coronado S, Fragoso G, Sahni H, Gustafson S, Buetow K (2003) caCORE: a common infrastructure for cancer informatics. Bioinformatics 19:2404–2412PubMedCrossRefGoogle Scholar
  7. CVRG (2009) The CardioVascular Research Grid (cvrg) 2009. From http://www.cvrgrid.org
  8. De Roure D, Goble C, Stevens R (2009) The design and realisation of the myExperiment virtual research environment for social sharing of workflows. Future Generation Comput Syst 25:561–567CrossRefGoogle Scholar
  9. DICOM (2009) The digital imaging and communications in medicine standard 2009. From http://medical.nema.org/
  10. Foster I (2006) Globus toolkit version 4: software for service-oriented systems. J Comput Sci Technol 21:523–530CrossRefGoogle Scholar
  11. Foster I, Kesselman C (1997) Globus: a metacomputing infrastructure toolkit. Int J High Perform Comput Appl 11:115–128CrossRefGoogle Scholar
  12. Foster I, Kesselman C (eds) (1999) The grid: blueprint for a new computing infrastructure. Morgan Kaufmann, San Francisco, CAGoogle Scholar
  13. Foster I, Kesselman C, Tsudik G et al (1998) A security architecture for computational grids. In: Proceedings of the 5th ACM conference on computer and communications security conference. ACM, San Francisco, CA, pp 83–92Google Scholar
  14. Foster I, Kesselman C, Tuecke S (2001) The anatomy of the Grid: Enabling scalable virtual organizations. Int J Supercomput Appl 15:200–222CrossRefGoogle Scholar
  15. Foster I, Czajkowski K, Ferguson D et al (2005) Modeling and managing state in distributed systems: the role of OGSI and WSRF. Proc IEEE 93:604–612CrossRefGoogle Scholar
  16. Gardner D, Akil H, Ascoli G et al (2008) The neuroscience information framework: a data and knowledge environment for neuroscience. Neuroinformatics 6:149–160PubMedCrossRefGoogle Scholar
  17. Graham S, Simeonov S, Boubez T et al (2002) Building web services with Java: Making sense of XML, SOAP, WSDL, and UDDI. SAMS Publishing, Indianapolis, INGoogle Scholar
  18. Grethe J, Baru C, Gupta A et al (2005) Biomedical informatics research network: building a national collaboratory to hasten the derivation of new understanding and treatment of disease. Stud Health Technol Inform 112:100–109PubMedGoogle Scholar
  19. Gupta A, Bug W, Marenco L et al (2008) Federated access to heterogeneous information resources in the neuroscience information framework (NIF). Neuroinformatics 6:205–217PubMedCrossRefGoogle Scholar
  20. Hastings S, Langella S, Oster S et al (2004) Distributed data management and integration: the mobius project. In: Proceedings of the Global Grid Forum 11 (GGF11) semantic grid applications workshop, Honolulu, Hawaii, USA, pp 20–38Google Scholar
  21. Hastings S, Oster S, Langella S et al (2007) Introduce: an open source toolkit for rapid development of strongly typed grid services. J Grid Comput 5:407–427CrossRefGoogle Scholar
  22. HL7 (2009) Health Level Seven 2009. From http://www.hl7.org
  23. Hull D, Wolstencroft K, Stevens R et al (2006) Taverna: a tool for building and running workflows of services. Nucleic Acids Res 34(Web Server issue):729–732CrossRefGoogle Scholar
  24. Humphrey M, Wasson G (2005) Architectural foundations of WSRF.NET. Int J Web Serv Res 2:83–97CrossRefGoogle Scholar
  25. IHE (2009) Integrating the healthcare enterprise 2009. From http://www.ihe.net
  26. Janciak I, Kloner C, Brezany P (2008) Workflow enactment engine for WSRF-compliant services orchestration. In: The 9th IEEE/ACM international conference on grid computing, pp 1–8Google Scholar
  27. Kloppmann M, Konig D, Leymann F et al (2004) Business process choreography in websphere: combining the power of BPEL and J2EE. IBM Syst J 43:270–296CrossRefGoogle Scholar
  28. Langella S, Hastings S, Oster S et al (2008) Sharing data and analytical resources securely in a biomedical research grid environment. J Am Med Inform Assoc (JAMIA) 15:363–373CrossRefGoogle Scholar
  29. OGF (2009) The open grid forum 2009. From http://www.ogf.org
  30. Oster S, Hastings S, Langella S, Ervin D, Madduri R, Kurc T, Siebenlist F, Foster I, Shanbhag K, Covitz P, Saltz J (2007). Cagrid 1.0: a grid enterprise architecture for cancer research. In: Proceedings of the 2007 American medical informatics association (AMIA) annual symposium, Chicago, ILGoogle Scholar
  31. Oster S, Langella S, Hastings S et al (2008) Cagrid 1.0: an enterprise grid infrastructure for biomedical research. J Am Med Inform Assoc (JAMIA) 15:138–149CrossRefGoogle Scholar
  32. OWL (2009) The web ontology language 2009. From http://www.w3.org/TR/owl-features/
  33. Phillips J, Chilukuri R, Fragoso G et al (2006) The caCORE software development kit: Streamlining construction of interoperable biomedical information services. BMC Med Inform Decis Mak 6:2Google Scholar
  34. RDF (2009) The resource description framework standard 2009. From http://www.w3.org/RDF/
  35. RDFS (2009) The resource description framework schema standard 2009. From http://www.w3.org/TR/rdf-schema/
  36. Saltz J, Oster S, Hastings S et al (2006) caGrid: design and implementation of the core architecture of the cancer biomedical informatics grid. Bioinformatics 22:1910–1916PubMedCrossRefGoogle Scholar
  37. Saltz J, Hastings S, Langella S et al (2008a) A roadmap for cagrid, an enterprise grid architecture for biomedical research. Stud Health Technol Inform 138:224–237PubMedGoogle Scholar
  38. Saltz J, Kurc T, Hastings S et al (2008b) e-Science, caGrid, and translational biomedical research. IEEE Comput 41:58–66CrossRefGoogle Scholar
  39. Saltz J, Oster S, Hastings S et al (2008c) Translational research design templates, grid computing, and HPC. In: The 22nd IEEE international parallel and distributed processing symposium (IPDPS’08). IEEE, Miami, FLGoogle Scholar
  40. Santini S, Gupta A (2003) The role of Internet imaging in the biomedical informatics research network. In: Santini S, Schettini R (eds) Proceedings of SPIE, vol 5018. Internet Imaging IV, San Jose, CAGoogle Scholar
  41. Sarang P, Juric M, Mathew B (2006) Business process execution language for Web services BPEL and BPEL4WS, 2nd edn. Packt Publishing, BirminghamGoogle Scholar
  42. Solomonides A, Mcclatchey R, Odeh M et al (2003) Mammogrid and eDiamond: grids applications in mammogram analysis. In: Proceedings of the IADIS international conference: e-Society 2003, Lisbon, Portugal, pp 1032–1033Google Scholar
  43. Stevens R, Robinson A, Goble C (2003) myGrid: personalised bioinformatics on the information Grid. Bioinformatics 19:302–304CrossRefGoogle Scholar
  44. Stevens R, Mcentire R, Goble C et al (2004) myGrid and the drug discovery process. Drug Discov Today: BIOSILICO 2:140–148CrossRefGoogle Scholar
  45. WEEP (2009) The workflow enactment engine project 2009. From http://weep.gridminer.org
  46. Welch V, Siebenlist F, Foster I et al (2003) Security for grid services. In: 12th international symposium on high performance distributed computing (HPDC-12). IEEE, Washington, DCGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Tahsin Kurc
  • Ashish Sharma
  • Scott Oster
  • Tony Pan
  • Shannon Hastings
  • Stephen Langella
  • David Ervin
  • Justin Permar
  • Daniel Brat
  • T. J. Fitzgerald
  • James Purdy
  • Walter Bosch
  • Joel Saltz
    • 1
  1. 1.Center for Comprehensive InformaticsEmory UniversityAtlantaUSA

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