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Inferring time-delayed gene regulatory networks using cross-correlation and sparse regression

Mundra, Piyushkumar A., Zheng, Jie, Niranjan, Mahesan, Welsch, Roy E. and Rajapakse, Jagath C., (2013) Inferring time-delayed gene regulatory networks using cross-correlation and sparse regression Zhipeng, Cai, Eulenstein, Oliver, Janies, Daniel and Schwartz, Daniel (eds.) In Bioinformatics Research and Applications. vol. 7875, Springer Berlin Heidelberg., pp. 64-75. (doi:10.1007/978-3-642-38036-5_10).

Record type: Conference or Workshop Item (Paper)


Inferring a time-delayed gene regulatory network from microarray gene-expression is challenging due to the small numbers of time samples and requirements to estimate a large number of parameters. In this paper, we present a two-step approach to tackle this challenge: first, an unbiased cross-correlation is used to determine the probable list of time-delays and then, a penalized regression technique such as the LASSO is used to infer the time-delayed network. This approach is tested on several synthetic and one real dataset. The results indicate the efficacy of the approach with promising future directions.

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Published date: 2013
Venue - Dates: conference; 2013-01-01, 2013-01-01
Keywords: LASSO, gene regulation, time-delayed interactions, microarray analysis, cross-correlation
Organisations: Southampton Wireless Group


Local EPrints ID: 355506
ISBN: 978-3-642-38035-8
PURE UUID: 5ab8fe5b-9ede-4438-a879-680f5564a7de

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Date deposited: 02 Sep 2013 11:33
Last modified: 18 Jul 2017 03:47

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Author: Piyushkumar A. Mundra
Author: Jie Zheng
Author: Roy E. Welsch
Author: Jagath C. Rajapakse
Editor: Cai Zhipeng
Editor: Oliver Eulenstein
Editor: Daniel Janies
Editor: Daniel Schwartz

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