Disentangling multidimensional spatio-temporal data into their common and aberrant responses.

ABSTRACT: With the advent of high-throughput measurement techniques, scientists and engineers are starting to grapple with massive data sets and encountering challenges with how to organize, process and extract information into meaningful structures. Multidimensional spatio-temporal biological data sets such as time series gene expression with various perturbations over different cell lines, or neural spike trains across many experimental trials, have the potential to acquire insight about the dynamic behavior of the system. For this potential to be realized, we need a suitable representation to understand the data. A general question is how to organize the observed data into meaningful structures and how to find an appropriate similarity measure. A natural way of viewing these complex high dimensio

SUBMITTER: Chang YH 

PROVIDER: S-EPMC4406848 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature