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- Title
Advances to Bayesian network inference for generating causal networks from observational biological data.
- Authors
Yu, Jing; Smith, V Anne; Wang, Paul P; Hartemink, Alexander J; Jarvis, Erich D
- Abstract
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 collected from biological systems. Here, we use a simulation approach to make advances in our dynamic Bayesian network (DBN) inference algorithm, especially in the context of limited quantities of biological data.
- Publication
Bioinformatics (Oxford, England), 2004, Vol 20, Issue 18, p3594
- ISSN
1367-4803
- Publication type
Journal Article
- DOI
10.1093/bioinformatics/bth448