Constructing Treatment Portfolios Using Affinity Propagation

Constructing Treatment Portfolios Using Affinity Propagation,10.1007/978-3-540-78839-3_31,Delbert Dueck,Brendan J. Frey,Nebojsa Jojic,Vladimir Jojic,G

Constructing Treatment Portfolios Using Affinity Propagation   (Citations: 6)
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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 im- munological protection to a specified population of recipients. We refer to this task as 'treatment portfolio design' and formalize it as a facility location prob- lem. 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: se- lecting 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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