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Advances to Bayesian network inference for generating causal networks from observational biological data
2004
Bioinformatics
Motivation: Network inference algorithms are powerful computational tools for identifying putative causal interactions among variables from observational data. Bayesian network inference algorithms hold particular promise in that they can capture linear, non-linear, combinatorial, stochastic and other types of relationships among variables across multiple levels of biological organization. However, challenges remain when applying these algorithms to limited quantities of experimental data
doi:10.1093/bioinformatics/bth448
pmid:15284094
fatcat:5h5oqggowrhsjgxfdkrxthepua