NDLI: Reconstructing gene regulatory networks by integrating knockout and multifactorial perturbation data

Content Provider	IEEE Xplore Digital Library
Author	Jie Xiong Tong Zhou
Copyright Year	2014
Description	Author affiliation: Dept. of Autom., Tsinghua Univ., Beijing, China (Jie Xiong; Tong Zhou)
Abstract	Structure identification is investigated in this paper for a gene regulatory network (GRN) using two different types of steady state expression data. Using knockout expression data, direct regulating genes for an arbitrary gene are estimated by means of maximum likelihood estimation (MLE). And, using multifactorial perturbation data, a weight corresponding to a possible direct regulation is constructed. To take above information into consideration, the weight matrix is further adjusted based on these estimated direct regulatory relationships. Computation results with the DREAM4 In Silico data show that prediction performances are substantially improved by the suggested method, which reveals that information contained in both knockout data and multifactorial data is complementary. Furthermore, the high precision of the obtained most reliable predictions shows the suggested algorithm may be helpful in guiding biological experiment designs.
Starting Page	6862
Ending Page	6867
File Size	1218660
Page Count	6
File Format	PDF
ISBN	9789881563873
ISSN	19341768
DOI	10.1109/ChiCC.2014.6896130
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	TCCT, CAA
Subject Keyword	Algorithm design and analysis Maximum likelihood estimation Correlation regression analysis sparsity Steady-state likelihood maximization Equations multifactorial perturbation data correlation analysis gene regulation network knockout data Prediction algorithms topology estimation
Content Type	Text
Resource Type	Article

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