Haack, F. and Fackeldey, K. and Röblitz, S. and Scharkoi, O. and Weber, M. and Schmidt, B.
(2013)
*Adaptive spectral clustering with application to tripeptide conformation analysis.*
J. Chem. Phys., 139
(19).
p. 194110.

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Official URL: http://dx.doi.org/10.1063/1.4830409

## Abstract

A decomposition of a molecular conformational space into sets or functions (states) allows for a reduced description of the dynamical behavior in terms of transition probabilities between these states. Spectral clustering of the corresponding transition probability matrix can then reveal metastable conformations. The more states are used for the decomposition, the smaller the risk to cover multiple conformations with one state, which would make these conformations indistinguishable. However, since the computational complexity of the clustering algorithm increases quadratically with the number of states, it is desirable to have as few states as possible. To balance these two contradictory goals, we present an algorithm for an adaptive decomposition of the position space starting from a very coarse decomposition. The algorithm is applied to small data classification problems where is was shown to be superior to commonly used algorithms such as e.g. k-means. We also applied this algorithm to the conformation analysis of a tripeptide molecule where six-dimensional time series were successfully analyzed.

Item Type: | Article |
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Subjects: | Physical Sciences > Chemistry > Physical Chemistry Mathematical and Computer Sciences > Mathematics > Applied Mathematics |

Divisions: | Department of Mathematics and Computer Science > Institute of Mathematics > BioComputing Group |

ID Code: | 1305 |

Deposited By: | BioComp Admin |

Deposited On: | 10 Jul 2013 08:49 |

Last Modified: | 03 Mar 2017 14:41 |

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