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).

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Description/Abstract

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.

Item Type: Conference or Workshop Item (Paper)
Digital Object Identifier (DOI): doi:10.1007/978-3-642-38036-5_10
ISBNs: 9783642380358 (print)
9783642380365 (electronic)
Venue - Dates: conference; 2013-01-01, 2013-01-01
Keywords: LASSO, gene regulation, time-delayed interactions, microarray analysis, cross-correlation
Subjects: Q Science > QA Mathematics > QA76 Computer software
R Medicine > R Medicine (General)
Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases
Organisations: Southampton Wireless Group
ePrint ID: 355506
Date :
Date Event
2013Published
Date Deposited: 02 Sep 2013 11:33
Last Modified: 17 Apr 2017 15:08
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/355506

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