Estimation of Genetic Networks and Functional Structures Between Genes by Using Bayesian Networks and Nonparametric Regression

By Seiya Imoto, Takao Goto, Satoru Miyano. Pacific Symposium on Biocomputing, 2001

349 citations recorded by the source corpus.

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Advances to Bayesian network inference for generating causal networks from observational biological data (2004, background): “Second, discrete BNs typically use combinatorial interaction models, making it difficult to determine the sign (+/−) and relative magnitude of interactions between variables.”
D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery (2021, methodology): “Common scoring functions include the Bayesian Information Criterion (BIC) [66], the Minimum Description Length (as an approximation of Kolmogorov Complexity) [82, 114, 121], the Bayesian Gaussian equivalent (BGe) score [66], the Bayesian Dirichlet equivalence (BDe) score [91], the Bayesian Dirichlet”
Learning Bayesian networks: approaches and issues (2011, methodology): “Imoto et al. (2002) also include an MCMC simulation of their method, based on non-parametric regression.”
Generalized Score Functions for Causal Discovery (2018, methodology): “More recently, some other score-based methods [2, 4, 18, 19, 26] and hybrid methods [9, 31] have been proposed; they exploit some assumptions of the model class.”
Score-based causal learning in additive noise models (2013, methodology): “Maximum a posteriori estimation was used in the setting of non-linear models with Gaussian noise by Imoto et al. [2002].”