Name
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1504/IJDMB.2013.055499 | IJDMB |
Keywords | DocType | Volume |
classification, data mining, evaluation, molecular dynamics simulations | Journal | 8 |
Issue | ISSN | Citations |
2 | 1748-5673 | 6 |
PageRank | References | Authors |
0.61 | 30 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vasileios A. Tatsis | 1 | 13 | 3.08 |
| Christos Tjortjis | 2 | 173 | 24.40 |
| Panagiotis Tzirakis | 3 | 86 | 7.43 |