Evaluating data mining algorithms using molecular dynamics trajectories Online publication date: Mon, 20-Oct-2014
by Vasileios A. Tatsis; Christos Tjortjis; Panagiotis Tzirakis
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 8, No. 2, 2013
Abstract: Molecular dynamics simulations provide a sample of a molecule's conformational space. Experiments on the µs time scale, resulting in large amounts of data, are nowadays routine. Data mining techniques such as classification provide a way to analyse such data. In this work, we evaluate and compare several classification algorithms using three data sets which resulted from computer simulations, of a potential enzyme mimetic biomolecule. We evaluated 65 classifiers available in the well-known data mining toolkit Weka, using 'classification' errors to assess algorithmic performance. Results suggest that: (i) 'meta' classifiers perform better than the other groups, when applied to molecular dynamics data sets; (ii) Random Forest and Rotation Forest are the best classifiers for all three data sets; and (iii) classification via clustering yields the highest classification error. Our findings are consistent with bibliographic evidence, suggesting a 'roadmap' for dealing with such data.
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