Inferring qualitative relations in genetic networks and metabolic pathways

By Tatsuya Akutsu, Satoru Miyano, S. Kuhara. Bioinform., 2000

407 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/829404430c5f2bc24317d7457bc1ac5a34737b57

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In Molecular Biology

Citations in the corpus

A Linear Representation of Dynamics of Boolean Networks (2010, background): “The Boolean network, first introduced by Kauffman [25], and then developed by [2]–[4], [15], [19], [22], [32], [33] and many others, becomes a powerful tool in describing, analyzing, and simulating the cellular networks.”
Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling (2014, background): “…dynamic GRNs include boolean network, Bayesian network, hidden Markov models among others (Liang, Fuhrman, and Somogyi 1998; Murphy and Mian 1999; Akutsu, Miyano, and Kuhara 2000; Friedman et al. 2000; Hartemink et al. 2001; Shmulevich et al. 2002; Zou and Conzen 2005; Gupta, Qu, and Ibrahim…”
Current approaches to gene regulatory network modelling (2007, background): “[72] and Probabilistic Boolean Networks by Shmulevich et al .”
Advances to Bayesian network inference for generating causal networks from observational biological data (2004, methodology): “A variety of network inference algorithms have recently been used to identify gene regulatory networks from observational gene expression data (Akutsu et al., 2000; Arkinet al., 1997; D’haeseleer et al., 1999; Friedmanet al., 2000; Gardner et al., 2003; Harteminket al., 2001; Lianget al., 1998; Weav”
Analysis and control of Boolean networks: A semi-tensor product approach (2010, methodology): “W[2,3] =  1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1  (11) (21) (12) (22) (13) (23) .”
Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks (2002, background): “To that end, much recent work has gone into identifying the structure of gene regulatory networks from expression data (Liang et al., 1998; Akutsu et al., 1998, 1999; D'Haeseleer et al., 2000; Akutsu et al., 2000; Shmulevich et al., 2001).”
Inferring gene networks from time series microarray data using dynamic Bayesian networks (2003, background): “Equations ( 1 ) and (2) hold when we use density or probability functions instead of probabilistic measure.”
On Learning Gene Regulatory Networks Under the Boolean Network Model (2003, background): “In order to cope with noisy expression patterns, (Akutsu et al., 2000) have proposed a robust inference algorithm for so-called noisy Boolean networks.”