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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Mundra, P.A., Zheng, J., Niranjan, M., Welsch, R.E., Rajapakse, J.C. (2013). Inferring Time-Delayed Gene Regulatory Networks Using Cross-Correlation and Sparse Regression. In: Cai, Z., Eulenstein, O., Janies, D., Schwartz, D. (eds) Bioinformatics Research and Applications. ISBRA 2013. Lecture Notes in Computer Science(), vol 7875. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38036-5_10
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DOI: https://doi.org/10.1007/978-3-642-38036-5_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38035-8
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