dc.contributor.authorXia, Kelin
dc.contributor.authorZhao, Zhixiong
dc.contributor.authorWei, Guo-Wei
dc.identifier.citationXia, K., Zhao, Z., & Wei, G.-W. (2015). Multiresolution persistent homology for excessively large biomolecular datasets. The Journal of Chemical Physics, 143(13), 134103-.en_US
dc.description.abstractAlthough persistent homology has emerged as a promising tool for the topological simplification of complex data, it is computationally intractable for large datasets. We introduce multiresolution persistent homology to handle excessively large datasets. We match the resolution with the scale of interest so as to represent large scale datasets with appropriate resolution. We utilize flexibility-rigidity index to access the topological connectivity of the data set and define a rigidity density for the filtration analysis. By appropriately tuning the resolution of the rigidity density, we are able to focus the topological lens on the scale of interest. The proposed multiresolution topological analysis is validated by a hexagonal fractal image which has three distinct scales. We further demonstrate the proposed method for extracting topological fingerprints from DNA molecules. In particular, the topological persistence of a virus capsid with 273 780 atoms is successfully analyzed which would otherwise be inaccessible to the normal point cloud method and unreliable by using coarse-grained multiscale persistent homology. The proposed method has also been successfully applied to the protein domain classification, which is the first time that persistent homology is used for practical protein domain analysis, to our knowledge. The proposed multiresolution topological method has potential applications in arbitrary data sets, such as social networks, biological networks, and graphs.en_US
dc.format.extent12 p.en_US
dc.relation.ispartofseriesThe Journal of Chemical Physicsen_US
dc.rights© 2015 American Institute of Physics. This paper was published in The Journal of Chemical Physics and is made available as an electronic reprint (preprint) with permission of American Institute of Physics. The published version is available at: [http://dx.doi.org/10.1063/1.4931733]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.en_US
dc.subjectMultiscale methodsen_US
dc.titleMultiresolution persistent homology for excessively large biomolecular datasetsen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen_US
dc.description.versionPublished versionen_US

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