@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}
}

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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  - 

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