Identifying localized changes in large systems: Change-point detection for biomolecular simulations.

ABSTRACT: Research on change-point detection, the classical problem of detecting abrupt changes in sequential data, has focused predominantly on datasets with a single observable. A growing number of time series datasets, however, involve many observables, often with the property that a given change typically affects only a few of the observables. We introduce a general statistical method that, given many noisy observables, detects points in time at which various subsets of the observables exhibit simultaneous changes in data distribution and explicitly identifies those subsets. Our work is motivated by the problem of identifying the nature and timing of biologically interesting conformational changes that occur during atomic-level simulations of biomolecules such as proteins. This problem has prove

SUBMITTER: Fan Z 

PROVIDER: S-EPMC4475967 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature