Learning Dynamic Systems from Time-series Data - An Application to Gene Regulatory Networks

Ivo J. P. M. Timoteo, Sean B. Holden

2015

Abstract

We propose a local search approach for learning dynamic systems from time-series data, using networks of differential equations as the underlying model. We evaluate the performance of our approach for two scenarios: first, by comparing with an l1-regularization approach under the assumption of a uniformly weighted network for identifying systems of masses and springs; and then on the task of learning gene regulatory networks, where we compare it with the best performers in the DREAM4 challenge using the original dataset for that challenge. Our method consistently improves on the performance of the other methods considered in both scenarios.

Paper Citation

in Harvard Style

J. P. M. Timoteo I. and B. Holden S. (2015). Learning Dynamic Systems from Time-series Data - An Application to Gene Regulatory Networks . In Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM, ISBN 978-989-758-077-2, pages 324-332. DOI: 10.5220/0005282303240332

in Bibtex Style

@conference{icpram15,
author={Ivo J. P. M. Timoteo and Sean B. Holden},
title={Learning Dynamic Systems from Time-series Data - An Application to Gene Regulatory Networks},
booktitle={Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,},
year={2015},
pages={324-332},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005282303240332},
isbn={978-989-758-077-2},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,
TI - Learning Dynamic Systems from Time-series Data - An Application to Gene Regulatory Networks
SN - 978-989-758-077-2
AU - J. P. M. Timoteo I.
AU - B. Holden S.
PY - 2015
SP - 324
EP - 332
DO - 10.5220/0005282303240332