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Common Misconceptions and Future Directions for AI in Medicine: A Physician-Data Scientist Perspective Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Bayesian Clustering for HIV1 Protease Inhibitor Contact Maps

Authors : Sandhya Prabhakaran, Julia E. Vogt

Published in: Artificial Intelligence in Medicine

Publisher: Springer International Publishing

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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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previous chapter Extracting Food-Drug Interactions from Scientific Literature: Tackling Unspecified Relation
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Literature
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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)CrossRef
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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)
Metadata
Title
Bayesian Clustering for HIV1 Protease Inhibitor Contact Maps
Authors
Sandhya Prabhakaran
Julia E. Vogt
Copyright Year
2019
Book
Artificial Intelligence in Medicine

Print ISBN: 978-3-030-21641-2

Electronic ISBN: 978-3-030-21642-9

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-21642-9_35

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