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Constructing Treatment Portfolios Using Affinity Propagation
- Source :
- Lecture Notes in Computer Science ISBN: 9783540788386, RECOMB
- Publication Year :
- 2008
- Publisher :
- Springer Berlin Heidelberg, 2008.
-
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.
- Subjects :
- education.field_of_study
Mathematical optimization
business.industry
Small number
Population
Biology
Machine learning
computer.software_genre
Facility location problem
Portfolio
Affinity propagation
Artificial intelligence
education
business
Cluster analysis
Greedy algorithm
computer
Selection (genetic algorithm)
Subjects
Details
- ISBN :
- 978-3-540-78838-6
- ISBNs :
- 9783540788386
- Database :
- OpenAIRE
- Journal :
- Lecture Notes in Computer Science ISBN: 9783540788386, RECOMB
- Accession number :
- edsair.doi...........2656c87c55ae54904c2f2c6278862ae7