Bayesian clustering for HIV1 protease inhibitor contact maps Conference Paper
| Authors: | Prabhakaran, S.; Vogt, J. E. |
| Title: | Bayesian clustering for HIV1 protease inhibitor contact maps |
| Conference Title: | 17th Conference on Artificial Intelligence in Medicine (AIME 2019) |
| 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. © Springer Nature Switzerland AG 2019. |
| Keywords: | artificial intelligence; clustering; medical informatics; bayesian nonparametrics; pairwise distances; bayesian clustering; hiv-1 protease inhibitors; overlapping clusters; probabilistic modeling; protein contact maps |
| Journal Title | Lecture Notes in Computer Science |
| Volume: | 11526 |
| Conference Dates: | 2019 Jun 26-29 |
| Conference Location: | Poznan, Poland |
| ISBN: | 0302-9743 |
| Publisher: | Springer |
| Date Published: | 2019-01-01 |
| Start Page: | 281 |
| End Page: | 285 |
| Language: | English |
| DOI: | 10.1007/978-3-030-21642-9_35 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | Source: Scopus |
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