Dimensionality Reduction Techniques for Protein Folding Trajectories

TL;DRAbstract

In our work we analyze large and high dimensional data from protein folding simulations.The main goals are to extract the underlying dimensionality, to find a small number of features that describe the data with high accuracy and to find interesting clusters in the data: in this work we treat this as a problem of dimensionality reduction.Dimensionality reduction aims to find a mapping of the original space into a space of a few interesting dimensions, which the user then can use for interpretation and analysis.We study modern dimensionality reduction techniques and combine them with promising distance measures, suitable for the description of dissimilarities between the data points generated by the package ProFASi -a Protein Folding and Aggregation Simulator.

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