Constructing Treatment Portfolios Using Affinity Propagation

  • Delbert Dueck
  • Brendan J. Frey
  • Nebojsa Jojic
  • Vladimir Jojic
  • Guri Giaever
  • Andrew Emili
  • Gabe Musso
  • Robert Hegele
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4955)

Abstract

A key problem of interest to biologists and medical researchers is the selection of a subset of queries or treatments that provide maximum utility for a population of targets. For example, when studying how gene deletion mutants respond to each of thousands of drugs, it is desirable to identify a small subset of genes that nearly uniquely define a drug ‘footprint’ that provides maximum predictability about the organism’s response to the drugs. As another example, when designing a cocktail of HIV genome sequences to be used as a vaccine, it is desirable to identify a small number of sequences that provide maximum immunological protection to a specified population of recipients. We refer to this task as ‘treatment portfolio design’ and formalize it as a facility location problem. Finding a treatment portfolio is NP-hard in the size of portfolio and number of targets, but a variety of greedy algorithms can be applied. We introduce a new algorithm for treatment portfolio design based on similar insights that made the recently-published affinity propagation algorithm work quite well for clustering tasks. We demonstrate this method using the two problems described above: selecting a subset of yeast genes that act as a drug-response footprint, and selecting a subset of vaccine sequences that provide maximum epitope coverage for an HIV genome population.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315, 972–976 (2007)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Frey, B.J., Dueck, D.: Affinity propagation and the vertex substitution heuristic. Science (in press)Google Scholar
  3. 3.
    MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proc. 5th Berkeley Symp. on Mathematical Statistics and Probability, pp. 281–297. Univ. of California Press (1967)Google Scholar
  4. 4.
    Balinksi, M.L.: On finding integer solutions to linear programs. In: Proc. IBM Scientific Computing Symp. on Combinatorial Problems, pp. 225–248 (1966)Google Scholar
  5. 5.
    Charikar, M., Guha, S., Tardos, A., Shmoys, D.B.: A constant-factor approximation algorithm for the k-median problem. J. Comp. and Sys. Sci. 65(1), 129–149 (2002)CrossRefMathSciNetMATHGoogle Scholar
  6. 6.
    Pierce, S.E., Fung, E.L., Jaramillo, D.F., Chu, A.M., Davis, R.W., Nislow, C., Giaever, G.: A unique and universal molecular barcode array. Nature Methods 3(8), 601–603 (2006)CrossRefGoogle Scholar
  7. 7.
    Maere, S., Heymans, K., Kuiper, M.: BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 21, 3448–3449 (2005)CrossRefGoogle Scholar
  8. 8.
    Jojic, N., Jojic, V., Frey, B., Meek, C., Heckerman, D.: Using epitomes to model genetic diversity: Rational design of HIV vaccine cocktails. NIPS 18, 587–594 (2005)Google Scholar
  9. 9.
    Nickle, D.C., et al.: Coping with Viral Diversity in HIV Vaccine Design. PLoS Computational Biology 3(4), e75 (2007)CrossRefGoogle Scholar
  10. 10.
    Fischer, W., Perkins, S., et al.: Polyvalent vaccines for optimal coverage of potential T-cell epitopes in global HIV-1 variants. Nature Medicine 13, 100–106 (2006)CrossRefGoogle Scholar
  11. 11.
    Mallal, S.: The Western Australian HIV Cohort Study, Perth, Australia. Journal of Acquired Immune Deficiency Syndromes and Human Retrovirology 17(Suppl. 1), 23–27 (1998)Google Scholar
  12. 12.
    Jojic, V.: Algorithms for rational vaccine design. Ph.D. Thesis, University of Toronto (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Delbert Dueck
    • 1
  • Brendan J. Frey
    • 1
    • 2
  • Nebojsa Jojic
    • 3
  • Vladimir Jojic
    • 1
    • 3
    • 4
  • Guri Giaever
    • 2
  • Andrew Emili
    • 2
  • Gabe Musso
    • 2
  • Robert Hegele
    • 5
  1. 1.Electrical and Computer EngineeringUniversity of TorontoCanada
  2. 2.Center for Cellular and Biomolecular ResearchUniversity of TorontoCanada
  3. 3.Machine Learning and StatisticsMicrosoft ResearchRedmondUSA
  4. 4.Computer ScienceStanford UniversityUSA
  5. 5.Cardiovascular Genetics LaboratoryRobarts Research InstituteLondonCanada

Personalised recommendations