Reverse engineering gene networks using singular value decomposition and robust regression

By M. K. S. Yeung, Jesper N. Tegnér, James J. Collins. Proceedings of the National Academy of Sciences of the United States of America, 2002

687 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0acc508d0066a2eb16feefed6f9a8ffa0bb5227f

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In Management Science and Operations Research

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Citations in the corpus

Inferring cellular networks – a review (2007, neutral): “Several papers [107-110] use multiple regression to model the response of genes and proteins to external perturbations.”
Gene regulatory network inference: Data integration in dynamic models - A review (2009, methodology): “Note that the number and allocation of time-points for sampling affects the performance of the GRN inference as was studied using synthetic data (Yeung et al., 2002; Geier et al., 2007).”
TIGRESS: Trustful Inference of Gene REgulation using Stability Selection (2012, neutral): “of its TG in subsequent times [1-11].”
RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse (2015, background): “TFs and downstream genes (5)], the low accuracy of these existing methods due to the curse of dimensionality significantly limits their applications in practice (6).”
Molecular ecological network analyses (2012, methodology): “and proteins, differential equation-based network methods [9–12], Bayesian network methods [13,14], and rele-”
Analysis of global control of Escherichia coli carbohydrate uptake (2007, positive): “The idea of using a variant of the singular value decomposition for decomposing an adjacency matrix has been proposed by several authors [42-45].”
Linear Model Regression on Time-series Data: Non-asymptotic Error Bounds and Applications (2018, background): “Symmetric system matrices appear in a wide range of applications where interactions leading to the dynamics is bidirectional; such systems are of interest in biological networks [16], social interactions [17], robotic swarms [18], [19], and networks security [20].”
Ecological Modeling from Time-Series Inference: Insight into Dynamics and Stability of Intestinal Microbiota (2013, methodology): “A related approach based on linear ordinary differential equations has already been applied to gene-interaction networks [41–44].”
Inference of gene regulatory networks and compound mode of action from time course gene expression profiles (2006, background): “Different experimental and computational methods have been proposed to tackle the network identification problem (Tong et al., 2002; Lee et al., 2002; Ideker et al., 2001; Davidson et al., 2002; Arkin et al., 1997; Yeung et al., 2002).”
A computational algebra approach to the reverse engineering of gene regulatory networks. (2003, methodology): “Then, as in (Yeung et al., 2002), we choose a particular network.”
Geometric Interpretation of Gene Coexpression Network Analysis (2008, methodology): “Here we use the singular value decomposition [44,45,48] since this will allow us to define a simple measure of factorizability (Equation 24).”
Observing and Interpreting Correlations in Metabolic Networks (2003, methodology): “Thus, similar to the approach developed by Yeung et al. (2002), one may optimize the solution to provide a Jacobian with a maximal number of 'zero' elements.”
Recent Developments in Parameter Estimation and Structure Identification of Biochemical and Genomic Systems (2009, methodology): “Other methods which were developed recently for inverse problems in biological systems are discussed in [139,205–210].”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, methodology): “This is one reason, why combination of various methods (or the use of iterative approaches) may outperform individual methodologies (Liang et al., 1998a; Akutsu et al., 1999; Ideker et al., 2000; Kholodenko et al., 2002; Yeung et al., 2002; Segal et al., 2003; Tegnér et al., 2003; Friedman, 2004; Te”
Singular value decomposition and principal component analysis (2003, methodology): “Applications of PCA and/or SVD to gene expression data have been published in (Alter et al., 2000; Holter et al., 2000; Holter et al., 2001; Raychaudhuri et al., 2000; Troyanskaya et al., 2001; Yeung and Ruzzo, 2001; Yeung et al., 2002).”