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Bayesian Clustering for HIV1 Protease Inhibitor Contact Maps

  • Sandhya PrabhakaranEmail author
  • Julia E. Vogt
Conference paper
  • 796 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11526)

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.

Keywords

Bayesian nonparametrics Clustering Medical informatics 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computational and Systems Biology ProgramMemorial Sloan Kettering Cancer CenterNew YorkUSA
  2. 2.Department of Computer ScienceETH ZurichZurichSwitzerland

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