NDLI: Reconstruction of gene regulatory networks from short time series high throughput data: Review and new findings

Content Provider	IEEE Xplore Digital Library
Author	Wu, H.C. Zhang, L. Chan, S.C.
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. & Electron. Eng., Univ. of Hong Kong, Hong Kong, China (Wu, H.C.; Zhang, L.; Chan, S.C.)
Abstract	The reconstruction of gene regulatory networks (GRNs) helps to improve the understanding of underlying molecular mechanisms. Many important biological phenomena, such as genetic events involved in cancer proliferation, have been attributed to these correlated gene expressions. The identification of these interactions, some of which carry signatures to clinical relevant physiological effects, sheds light on the development of various clinical applications. For example, breast cancer metastasis can be inferred from the gene networks reconstructed from high throughput data. However, the DNA microarray data usually contain large number of genes but small number of samples, thus the incorporation of the extra dimension in time may lead to further complications in capturing the gene regulations due to the curse of dimensionality. This review focuses on introducing the signal processing community the concept of GRN reconstruction. In particular, we highlight state-of-the-art methodologies and the latest challenges in GRN reconstruction from short time course high throughput data.
Starting Page	733
Ending Page	738
File Size	461720
Page Count	6
File Format	PDF
ISBN	9781479946129
ISSN	21653577
DOI	10.1109/ICDSP.2014.6900761
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Gene expression Bayes methods Signal processing algorithms Digital signal processing Mathematical model Estimation time-series Gene regulatory networks (GRNs) microarray time-course large-scale
Content Type	Text
Resource Type	Article

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