A direct approach to conformational dynamics based on hybrid Monte Carlo
Journal of Computational Physics - Special issue on computational molecular biophysics
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visualizing dynamic molecular conformations
Proceedings of the conference on Visualization '02
Similarity-based clustering by left-stochastic matrix factorization
The Journal of Machine Learning Research
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In the present paper we explain the basic ideas of Robust Perron Cluster Analysis (PCCA+) and exemplify the different application areas of this new and powerful method. Recently, Deuflhard and Weber [5] proposed PCCA+ as a new cluster algorithm in conformation dynamics for computational drug design. This method was originally designed for the identification of almost invariant subsets of states in a Markov chain. As an advantage, PCCA+ provides an indicator for the number of clusters. It turned out that PCCA+ can also be applied to other problems in life science. We are going to show how it serves for the clustering of gene expression data stemming from breast cancer research [20]. We also demonstrate that PCCA+ can be used for the clustering of HIV protease inhibitors corresponding to their activity. In theoretical chemistry, PCCA+ is applied to the analysis of metastable ensembles in monomolecular kinetics, which is a tool for RNA folding [21].