Abstract
We present a probabilistic model for clustering which enables the modeling of overlapping clusters where objects are only available as pairwise distances. Examples of such distance data are genomic string alignments, or protein contact maps. In our clustering model, an object has the freedom to belong to one or more clusters at the same time. By using an IBP process prior, there is no need to explicitly fix the number of clusters, as well as the number of overlapping clusters, in advance. In this paper, we demonstrate the utility of our model using distance data obtained from HIV1 protease inhibitor contact maps.
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References
Achenbach, C.J., Darin, K.M., Murphy, R.L., Christine, K.: Atazanavir/ritonavir-based combination antiretroviral therapy for treatment of HIV-1 infection in adults. Future Virol. 6(2), 157–177 (2011)
Berman, H.M., et al.: The protein data bank. Nucleic Acids Res. 28, 235–242 (2000)
Bernardino, J.I., Arribas, J.R.: Antiviral therapy. Infect. Dis. 4, 918–926 (2011)
Griffiths, T.L., Ghahramani, Z.: Infinite latent feature models and the Indian buffet process, May 2005
Heller, K.A., Ghaharamani, Z.: A nonparametric Bayesian approach to modeling overlapping clusters. In: AISTATS (2007)
Li, M., Vitányi, P.: An Introduction to Kolmogorov Complexity and Its Applications. Texts in Computer Science. Springer, New York (2008). https://doi.org/10.1007/978-0-387-49820-1
Zhengtong, L., Chu, Y., Wang, Y.: HIV protease inhibitors: a review of molecular selectivity and toxicity. HIV AIDS (Auckl.) 7, 95 (2015)
Schölkopf, B., Smola, A.J., et al.: Learning with kernels: support vector machines, regularization, optimization, and beyond (2002)
Streich, A.P., Frank, M., Buhmann, J.M.: Multi-assignment clustering for Boolean data. In: ICML (2009)
Vitányi, P.M.B., Balbach, F.J., Cilibrasi, R.L., Li, M.: Normalized information distance. In: Emmert-Streib, F., Dehmer, M. (eds.) Information Theory and Statistical Learning, pp. 45–82. Springer, Boston (2009). https://doi.org/10.1007/978-0-387-84816-7_3
Vogt, J.E., Prabhakaran, S., Fuchs, T.J., Roth, V.: The translation-invariant Wishart-Dirichlet process for clustering distance data. In: ICML, pp. 1111–1118 (2010)
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Prabhakaran, S., Vogt, J.E. (2019). Bayesian Clustering for HIV1 Protease Inhibitor Contact Maps. In: Riaño, D., Wilk, S., ten Teije, A. (eds) Artificial Intelligence in Medicine. AIME 2019. Lecture Notes in Computer Science(), vol 11526. Springer, Cham. https://doi.org/10.1007/978-3-030-21642-9_35
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DOI: https://doi.org/10.1007/978-3-030-21642-9_35
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