RT Journal Article
SR Electronic
T1 Automatic time-series phenotyping using massive feature extraction
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 081463
DO 10.1101/081463
A1 Fulcher, B. D.
A1 Jones, N. S.
YR 2016
UL http://biorxiv.org/content/early/2016/10/17/081463.abstract
AB Phenotype measurements frequently take the form of time series, but we currently lack a systematic method for relating these complex data streams to scientifically meaningful outcomes, such as relating the movement dynamics of a model organism to their genotype, or measurements of brain dynamics of a patient to their disease diagnosis. Here we report a new tool, hctsa, that automatically selects interpretable and useful properties of time series by comparing over 7 700 time-series features drawn from diverse scientific literatures. Using exemplar applications to high throughput phenotyping experiments, we show how hctsa allows researchers to leverage decades of time-series research to understand and quantify informative structure in time-series data.