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dc.contributor.authorChen, Haifenen
dc.contributor.authorMundra, Piyushkumar Aen
dc.contributor.authorZhao, Li Naen
dc.contributor.authorLin, Fengen
dc.contributor.authorZheng, Jieen
dc.identifier.citationChen, H., Mundra, P. A., Zhao, L. N., Lin, F., & Zheng, J. (2014). Highly sensitive inference of time-delayed gene regulation by network deconvolution. BMC Systems Biology, 8(Suppl 4), S6-.en
dc.description.abstractBackground: Gene regulatory network (GRN) is a fundamental topic in systems biology. The dynamics of GRN can shed light on the cellular processes, which facilitates the understanding of the mechanisms of diseases when the processes are dysregulated. Accurate reconstruction of GRN could also provide guidelines for experimental biologists. Therefore, inferring gene regulatory networks from high-throughput gene expression data is a central problem in systems biology. However, due to the inherent complexity of gene regulation, noise in measuring the data and the short length of time-series data, it is very challenging to reconstruct accurate GRNs. On the other hand, a better understanding into gene regulation could help to improve the performance of GRN inference. Time delay is one of the most important characteristics of gene regulation. By incorporating the information of time delays, we can achieve more accurate inference of GRN. Results: In this paper, we propose a method to infer time-delayed gene regulation based on cross-correlation and network deconvolution (ND). First, we employ cross-correlation to obtain the probable time delays for the interactions between each target gene and its potential regulators. Then based on the inferred delays, the technique of ND is applied to identify direct interactions between the target gene and its regulators. Experiments on real-life gene expression datasets show that our method achieves overall better performance than existing methods for inferring time-delayed GRNs. Conclusion: By taking into account the time delays among gene interactions, our method is able to infer GRN more accurately. The effectiveness of our method has been shown by the experiments on three real-life gene expression datasets of yeast. Compared with other existing methods which were designed for learning time-delayed GRN, our method has significantly higher sensitivity without much reduction of specificity.en
dc.format.extent10 p.en
dc.relation.ispartofseriesBMC Systems Biologyen
dc.rights© 2014 Chen et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.en
dc.subjectGRN inferenceen
dc.subjecttime delayen
dc.subjectnetwork deconvolutionen
dc.titleHighly sensitive inference of time-delayed gene regulation by network deconvolutionen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen
dc.description.versionPublished versionen
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