title: Evaluating data mining algorithms using molecular dynamics trajectories.
abstract: Molecular dynamics simulations provide a sample of a molecule's conformational space. Experiments on the mus 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.
authors.affiliation: Department of Engineering Informatics and Telecommunications, Section of Applied Informatics, University of Western Macedonia, Vermiou & Ligeris, Kozani 50100, Greece. btatsis@uowm.gr
authors.forename: Vasileios A
authors.initials: VA
authors.lastname: Tatsis
authors.forename: Christos
authors.initials: C
authors.lastname: Tjortjis
authors.forename: Panagiotis
authors.initials: P
authors.lastname: Tzirakis
mesh_terms: D000465:Algorithms; D057225:Data Mining / Q000379:methods*; D056004:Molecular Dynamics Simulation; D018716:Molecular Mimicry; D010455:Peptides / Q000737:chemistry; D057057:Serine Proteases / Q000737:chemistry
chemical_list: D010455:Peptides; D057057:Serine Proteases