Cancer informatics

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Cancer informatics is a multidisciplinary field of science that "deals with the resources, devices, and methods required to optimize the acquisition, storage, retrieval, and use of information in cancer" research and treatment.[1] Like many other fields of science, researchers in cancer biology have seen a dramatic increase in the amount of clinical and research data, in particular with genomic and molecular cancer data. While this data can benefit researchers' understanding of cancer behavior and development of better therapies, new and improved data management and analysis tools are needed. Cancer informatics attempts to provide those tools "that interconnect research, clinical activities, and data in an organized and efficient manner, with as broad a database as possible."[2] For many, the coupling of cancer informatics and other bioinformatics tools with computational modeling and statistical analysis will accelerate the goal of making cancer a more treatable if not curable disease.[3]

Application

Cancer informatics can help tackle problems and tasks such as the following[3][4]:

  • the development of computational diagnosis, prognosis, and predictive models
  • the development of standards for the entry, annotation, and sharing of clinical cancer data
  • the management and distribution of annotated molecular data for further research
  • the analysis of an individual tumor to determine it molecular phenotype
  • the development of a treatment plan based on a tumor's molecular phenotype
  • the tracking of the aberrations that drive a particular cancer's growth

Informatics

In the United States

In the U.S., the field of cancer informatics became much more noticeable with the late 1990s push by the National Cancer Institute's director Richard D. Klausner to create "a Cancer Informatics Infrastructure (CII) to enable the cancer research enterprise and to link it to the delivery of cancer care."[5]


External links

References

  1. "Cancer Informatics". National Cancer Registrars Association. http://www.ncra-usa.org/i4a/pages/index.cfm?pageid=3934. Retrieved 19 June 2014. 
  2. Nass, Sharyl J.; Wizemann, Theresa (eds.) (2012). "Chapter 2: Overview of the Cancer Informatics Landscape". Informatics Needs and Challenges in Cancer Research: Workshop Summary. National Academies Press. pp. 7–30. ISBN 9780309259484. http://books.google.com/books?id=qWuJAco_5R8C&pg=PA7. Retrieved 19 June 2014. 
  3. 3.0 3.1 Ochs, Michael F.; Casagrande, John T.; Davuluri, Ramana V. (2010). "Chapter 1: Biomedical Informatics for Cancer Research: Introduction". Biomedical Informatics for Cancer Research. Springer. pp. 3–16. ISBN 9781441957122. http://books.google.com/books?id=Ej8J_BZFON4C&printsec=frontcover. Retrieved 19 June 2014. 
  4. Salem, Abdel-Badeeh M. (2013). "Chapter 1: Machine Learning Applications in Cancer Informatics". Advances in Intelligent Analysis of Medical Data and Decision Support Systems. Studies in Computational Intelligence. Springer. pp. 1–14. ISBN 9783319000282. http://link.springer.com/chapter/10.1007%2F978-3-319-00029-9_1. Retrieved 19 June 2014. 
  5. Klausner, Richard D.; Silva, John S. (ed.) (2002). "Foreward". Cancer Informatics: Essential Technologies for Clinical Trials. Health Informatics. pp. vii–x. ISBN 9781461265474. http://books.google.com/books?id=DhNSheaOeDEC. Retrieved 19 June 2014.