caa a22 4500 605478309 CHVBK 20210128100405.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s10994-015-5516-x doi (NATIONALLICENCE)springer-10.1007/s10994-015-5516-x Probabilistic clustering of time-evolving distance data [Elektronische Daten] [Julia Vogt, Marius Kloft, Stefan Stark, Sudhir Raman, Sandhya Prabhakaran, Volker Roth, Gunnar Rätsch] We present a novel probabilistic clustering model for objects that are represented via pairwise distances and observed at different time points. The proposed method utilizes the information given by adjacent time points to find the underlying cluster structure and obtain a smooth cluster evolution. This approach allows the number of objects and clusters to differ at every time point, and no identification on the identities of the objects is needed. Further, the model does not require the number of clusters being specified in advance—they are instead determined automatically using a Dirichlet process prior. We validate our model on synthetic data showing that the proposed method is more accurate than state-of-the-art clustering methods. Finally, we use our dynamic clustering model to analyze and illustrate the evolution of brain cancer patients over time. The Author(s), 2015 Vogt Julia Computational Biology, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, 10065, New York, NY, USA aut Kloft Marius Department of Computer Science, Humboldt University of Berlin, Berlin, Germany aut Stark Stefan Computational Biology, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, 10065, New York, NY, USA aut Raman Sudhir Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland aut Prabhakaran Sandhya Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland aut Roth Volker Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland aut Rätsch Gunnar Computational Biology, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, 10065, New York, NY, USA aut Machine Learning Springer US; http://www.springer-ny.com 100/2-3(2015-09-01), 635-654 0885-6125 100:2-3<635 2015 100 10994 https://doi.org/10.1007/s10994-015-5516-x text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10994-015-5516-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Vogt Julia Computational Biology, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, 10065, New York, NY, USA aut NATIONALLICENCE 700 1- Kloft Marius Department of Computer Science, Humboldt University of Berlin, Berlin, Germany aut NATIONALLICENCE 700 1- Stark Stefan Computational Biology, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, 10065, New York, NY, USA aut NATIONALLICENCE 700 1- Raman Sudhir Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland aut NATIONALLICENCE 700 1- Prabhakaran Sandhya Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland aut NATIONALLICENCE 700 1- Roth Volker Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland aut NATIONALLICENCE 700 1- Rätsch Gunnar Computational Biology, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, 10065, New York, NY, USA aut NATIONALLICENCE 773 0- Machine Learning Springer US; http://www.springer-ny.com 100/2-3(2015-09-01), 635-654 0885-6125 100:2-3<635 2015 100 10994