Constructing Treatment Portfolios Using Affinity Propagation

  • Delbert Dueck
  • Brendan J. Frey
  • Nebojsa Jojic
  • Vladimir Jojic
  • Guri Giaever
  • Andrew Emili
  • Gabe Musso
  • Robert Hegele
Conference paper

DOI: 10.1007/978-3-540-78839-3_31

Volume 4955 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Dueck D. et al. (2008) Constructing Treatment Portfolios Using Affinity Propagation. In: Vingron M., Wong L. (eds) Research in Computational Molecular Biology. RECOMB 2008. Lecture Notes in Computer Science, vol 4955. Springer, Berlin, Heidelberg

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.

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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