Skip to main content
View expanded cover

Foundations of Biomedical Knowledge Representation pp 139–141Cite as

Personalised Medicine: Taking a New Look at the Patient

Part of the Lecture Notes in Computer Science book series (LNAI,volume 9521)

Abstract

Personalised medicine strives to identify the right treatment for the right patient at the right time, integrating different types of biological and environmental information.

Keywords

  • Feature Selection
  • Bayesian Network
  • Personalise Medicine
  • Molecular Diagnostics
  • Omics Data

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-28007-3_8
  • Chapter length: 3 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   59.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-28007-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   79.99
Price excludes VAT (USA)
Learn about institutional subscriptions

References

  1. Cavalli-Sforza, L.L., Feldman, M.W.: The application of molecular genetic approaches to the study of human evolution. Nat. Genet. 33, 266–275 (2003)

    CrossRef  Google Scholar 

  2. Collins, F.S., Morgan, M., Patrinos, A.: The human genome project: lessons from large-scale biology. Science 300(5167), 186–290 (2003)

    Google Scholar 

  3. Cooper, G.F., et al.: An efficient bayesian method for predicting clinical outcomes from genome-wide data. In: AMIA Annual Symposium Proceedings, pp. 127–131 (2010)

    Google Scholar 

  4. Emmer-Streib, F.: Personalized medicine: has it started yet? A reconstruction of the early history. Front. Genet. 3(313), 1–4 (2013)

    Google Scholar 

  5. Friedman, N., Linial, M., Nachman, I.: Using Bayesian networks to analyze expression data. J. Comput. Biol. 7, 601–620 (2000)

    CrossRef  Google Scholar 

  6. Ginsburg, G.S., McCarthy, J.J.: Personalized medicine: revolutionizing drug discovery and patient care. Trends Biotechnol. 19(12), 491–496 (2001)

    CrossRef  Google Scholar 

  7. Golub, T.R., et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439), 531–537 (1999)

    CrossRef  Google Scholar 

  8. Gygi, S.P., et al.: Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat. Biotechnol. 17, 994–999 (1999)

    CrossRef  Google Scholar 

  9. Hamburg, M.A., Collins, F.S.: The path to personalized medicine. New Engl. J. Med. 363, 301–304 (2010)

    CrossRef  Google Scholar 

  10. Ideker, T., et al.: Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 292(5518), 929–934 (2001)

    CrossRef  Google Scholar 

  11. Koller D., Sahami M.: Toward optimal feature selection. In: Proceedings of the 13th International Conference on Machine Learning (ICML), pp. 284–292 (1996)

    Google Scholar 

  12. Mourad, R., Sinoquet, C., Leray, P.: A hierarchical bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-wide association studies. BMC Bioinform. 12(16), 1–20 (2011)

    Google Scholar 

  13. Sachs, K., et al.: Causal protein-signaling networks derived from multiparameter single-cell data. Science 308(5721), 523–529 (2005)

    CrossRef  Google Scholar 

  14. Sawicki, M.P., et al.: Human genome project. Am. J. Surg. 165(2), 258–264 (1993)

    CrossRef  Google Scholar 

  15. Schadt, E.E., et al.: An integrative genomics approach to infer causal associations between gene expression and disease. Nat. Genet. 37(7), 710–717 (2005)

    CrossRef  Google Scholar 

  16. Scutari, M., Strimmer, K.: Introduction to graphical modelling. In: Balding, D.J., Stumpf, M., Girolami, M. (eds.) Handbook of Statistical Systems Biology. Wiley, Hoboken (2011)

    Google Scholar 

  17. Waring, J.F., et al.: Microarray analysis of hepatotoxins in vitro reveals a correlation between gene expression profiles and mechanisms of toxicity. Toxicol. Lett. 120(1–3), 359–368 (2001)

    CrossRef  Google Scholar 

  18. Weston, A.D., Hood, L.: Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine. J. Proteome Res. 3(2), 179–196 (2004)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Statistics, University of Oxford, Oxford, UK

    Marco Scutari

Authors
  1. Marco Scutari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marco Scutari .

Editor information

Editors and Affiliations

  1. Inst. for Computing and Inf. Sciences, Radboud University, Nijmegen, The Netherlands

    Arjen Hommersom

  2. Inst. for Computing and Inf. Sciences, Radboud University, Nijmegen, Gelderland, The Netherlands

    Peter J.F. Lucas

Rights and permissions

Reprints and Permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Scutari, M. (2015). Personalised Medicine: Taking a New Look at the Patient. In: Hommersom, A., Lucas, P. (eds) Foundations of Biomedical Knowledge Representation. Lecture Notes in Computer Science(), vol 9521. Springer, Cham. https://doi.org/10.1007/978-3-319-28007-3_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-28007-3_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28006-6

  • Online ISBN: 978-3-319-28007-3

  • eBook Packages: Computer ScienceComputer Science (R0)