Robust Perron Cluster Analysis for Various Applications in Computational Life Science

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].

• Type: Conference
• Year: 2005
• Where: COMPLIFE
• Authors: Marcus Weber, Susanna Kube
• Keywords: Applied Computing, COMPLIFE 2005, Computational Drug Design, Different Application Areas, Robust Perron Cluster