In this section
@article{Fulcher:2017:10.1016/j.cels.2017.10.001,
author = {Fulcher, B and Jones, NS},
doi = {10.1016/j.cels.2017.10.001},
journal = {Cell Systems},
pages = {527--531.e3},
title = {hctsa: A computational framework for automated timeseriesphenotyping using massive feature extraction},
url = {http://dx.doi.org/10.1016/j.cels.2017.10.001},
volume = {5},
year = {2017}
}
TY - JOUR
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 organisms to their genotype or measurements of brain dynamics of a patient to their disease diagnosis. Previous work addressed this problem by comparing implementations of thousands of diverse scientific time-series analysis methods in an approach termed highly comparative time-series analysis. Here, we introduce hctsa, a software tool for applying this methodological approach to data. hctsa includes an architecture for computing over 7,700 time-series features and a suite of analysis and visualization algorithms to automatically select useful and interpretable time-series features for a given application. Using exemplar applications to high-throughput phenotyping experiments, we show how hctsa allows researchers to leverage decades of time-series research to quantify and understand informative structure in time-series data.
AU - Fulcher,B
AU - Jones,NS
DO - 10.1016/j.cels.2017.10.001
EP - 531
PY - 2017///
SN - 2405-4712
SP - 527
TI - hctsa: A computational framework for automated timeseriesphenotyping using massive feature extraction
T2 - Cell Systems
UR - http://dx.doi.org/10.1016/j.cels.2017.10.001
UR - http://hdl.handle.net/10044/1/51795
VL - 5
ER -