Most of the members of this group are from the Statistics Section and Biomaths research group of the Department of Mathematics. Below you can find a list of research areas that members of this group are currently working on and/or would like to work on by applying their developed mathematical and statistical methods.

Research areas

Research areas

Systems Biology
Statistical genomics and Epidemiology
Medical Imaging
Precision and Stratified Medicine
Analysis of clinical trials, observational and longitudinal studies
Infectious Disease Epidemiology

Publications

Citation

BibTex format

@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}
}

RIS format (EndNote, RefMan)

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
VL - 5
ER -

Contact us

If you are interested in meeting with members of the group please contact Marina Evangelou