Revealing Structure of Complex Biological Systems Using Bayesian Networks

Smith, V. Anne

doi:10.1007/978-1-84996-396-1_9

Cited by 2 publications

(2 citation statements)

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“…30 The Bayesian approach, on the other hand, measures the conditional probability of the effect given the occurrence of the cause. 31,32 Studies indicated that the Bayesian method outperforms the other two approaches when the data length is short (i.e., with a smaller number of samples); 33 this better suits our case since the data set is small, with only 42 samples. The first step in causality detection in a Bayesian framework is learning the topology of the causal network, commonly called the Bayesian network (BN), followed by parameter learning.…”

Section: ■ Materials and Methodsmentioning

confidence: 76%

“…In the Granger causality detection approach, the improvement gained in predicting the effect due to incorporation of the cause variable as a predictor is used as an indication of a causal relationship . The Bayesian approach, on the other hand, measures the conditional probability of the effect given the occurrence of the cause. , Studies indicated that the Bayesian method outperforms the other two approaches when the data length is short (i.e., with a smaller number of samples); this better suits our case since the data set is small, with only 42 samples.…”

Section: Methodsmentioning

confidence: 95%

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A Bayesian Learning Approach to Modeling Pseudoreaction Networks for Complex Reacting Systems: Application to the Mild Visbreaking of Bitumen

Tefera

Jaramillo

Ranjan

et al. 2017

Ind. Eng. Chem. Res.

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A data-mining and Bayesian learning approach is used to model the reaction network of a low-temperature (150–400 °C) visbreaking process for field upgrading of oil sands bitumen. Obtaining mechanistic and kinetic descriptions for the chemistry involved in this process is a significant challenge because of the compositional complexity of bitumen and the associated analytical challenges. Lumped models based on a preconceived reaction network might be unsatisfactory in describing the key conversion steps of the actual process. Fourier transform infrared spectra of products produced at different operating conditions (temperature and time of processing) of the visbreaking process were collected. Bayesian agglomerative hierarchical cluster analysis was employed to obtain groups of pseudospecies with similar spectroscopic properties. Then, a Bayesian structure-learning algorithm was used to develop the corresponding reaction network. The final reaction network model was compared to the anticipated reaction network of thermal cracking of a model alkyl tricyclic naphthenoaromatic compound, and the agreement was encouraging. The reaction model also indicates that the outcome of thermal processing is the increase in lighter and more aliphatic products, which is consistent with experimental findings. Pseudokinetics were obtained for the reactions between the pseudospecies based on the estimated parameters of the Bayesian network. An attractive feature of the model is that it can be embedded into a process control system to perform real-time online analysis of the reactions both qualitatively and quantitatively.

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