Constructing treatment portfolios using affinity propagation

Authors:
Delbert Dueck;Brendan J. Frey;Nebojsa Jojic;Vladimir Jojic;Guri Giaever;Andrew Emili;Gabe Musso;Robert Hegele
Affiliations:
Electrical and Computer Engineering, University of Toronto, Canada;Electrical and Computer Engineering, University of Toronto, Canada and Center for Cellular and Biomolecular Research, University of Toronto, Canada;Machine Learning and Statistics, Microsoft Research, Redmond;Electrical and Computer Engineering, University of Toronto, Canada and Machine Learning and Statistics, Microsoft Research, Redmond and Computer Science, Stanford University;Center for Cellular and Biomolecular Research, University of Toronto, Canada;Center for Cellular and Biomolecular Research, University of Toronto, Canada;Center for Cellular and Biomolecular Research, University of Toronto, Canada;Cardiovascular Genetics Laboratory, Robarts Research Institute, London, Canada
Venue:
RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
Year:
2008

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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.