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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References
Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315, 972–976 (2007)
Frey, B.J., Dueck, D.: Affinity propagation and the vertex substitution heuristic. Science (in press)
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)
Balinksi, M.L.: On finding integer solutions to linear programs. In: Proc. IBM Scientific Computing Symp. on Combinatorial Problems, pp. 225–248 (1966)
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)
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)
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)
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)
Nickle, D.C., et al.: Coping with Viral Diversity in HIV Vaccine Design. PLoS Computational Biology 3(4), e75 (2007)
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)
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)
Jojic, V.: Algorithms for rational vaccine design. Ph.D. Thesis, University of Toronto (2007)
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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. https://doi.org/10.1007/978-3-540-78839-3_31
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DOI: https://doi.org/10.1007/978-3-540-78839-3_31
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