Inferring time-delayed gene regulatory networks using cross-correlation and sparse regression
Inferring time-delayed gene regulatory networks using cross-correlation and sparse regression
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.
LASSO, gene regulation, time-delayed interactions, microarray analysis, cross-correlation
978-3-642-38035-8
64-75
Springer Berlin, Heidelberg
Mundra, Piyushkumar A.
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Zheng, Jie
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Niranjan, Mahesan
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Welsch, Roy E.
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Rajapakse, Jagath C.
58406658-b8a1-40d8-9eaf-0e8f4685decf
2013
Mundra, Piyushkumar A.
0c406dd0-1bdf-4c3c-9b2f-df7a22151cc5
Zheng, Jie
93347b9a-c5fc-4f71-b1b8-825172e5bb9f
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Welsch, Roy E.
748a30c0-1343-4b7a-a118-5214cfda1b78
Rajapakse, Jagath C.
58406658-b8a1-40d8-9eaf-0e8f4685decf
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.
.
(doi:10.1007/978-3-642-38036-5_10).
Record type:
Conference or Workshop Item
(Paper)
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.
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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
Identifiers
Local EPrints ID: 355506
URI: http://eprints.soton.ac.uk/id/eprint/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: 15 Mar 2024 03:29
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Contributors
Author:
Piyushkumar A. Mundra
Author:
Jie Zheng
Author:
Mahesan Niranjan
Author:
Roy E. Welsch
Author:
Jagath C. Rajapakse
Editor:
Cai Zhipeng
Editor:
Oliver Eulenstein
Editor:
Daniel Janies
Editor:
Daniel Schwartz
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