2 research outputs found
Practical application of a Bayesian network approach to poultry epigenetics and stress
This work was supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 812777. We also greatly appreciate funding from the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (FORMAS) grants #2018-01074 and #2017-00946 to CG-B. FP appreciates funding from São Paulo Research Foundation (FAPESP, Brazil) projects #2016/20440-3 and #2018/13600-0.Background: Relationships among genetic or epigenetic features can be explored by learning probabilistic networks and unravelling the dependencies among a set of given genetic/epigenetic features. Bayesian networks (BNs) consist of nodes that represent the variables and arcs that represent the probabilistic relationships between the variables. However, practical guidance on how to make choices among the wide array of possibilities in Bayesian network analysis is limited. Our study aimed to apply a BN approach, while clearly laying out our analysis choices as an example for future researchers, in order to provide further insights into the relationships among epigenetic features and a stressful condition in chickens (Gallus gallus). Results: Chickens raised under control conditions (n = 22) and chickens exposed to a social isolation protocol (n = 24) were used to identify differentially methylated regions (DMRs). A total of 60 DMRs were selected by a threshold, after bioinformatic pre-processing and analysis. The treatment was included as a binary variable (control = 0; stress = 1). Thereafter, a BN approach was applied: initially, a pre-filtering test was used for identifying pairs of features that must not be included in the process of learning the structure of the network; then, the average probability values for each arc of being part of the network were calculated; and finally, the arcs that were part of the consensus network were selected. The structure of the BN consisted of 47 out of 61 features (60 DMRs and the stressful condition), displaying 43 functional relationships. The stress condition was connected to two DMRs, one of them playing a role in tight and adhesive intracellular junctions in organs such as ovary, intestine, and brain. Conclusions: We clearly explain our steps in making each analysis choice, from discrete BN models to final generation of a consensus network from multiple model averaging searches. The epigenetic BN unravelled functional relationships among the DMRs, as well as epigenetic features in close association with the stressful condition the chickens were exposed to. The DMRs interacting with the stress condition could be further explored in future studies as possible biomarkers of stress in poultry species.Publisher PDFPeer reviewe
Exact Learning Augmented Naive Bayes Classifier
Earlier studies have shown that classification accuracies of Bayesian
networks (BNs) obtained by maximizing the conditional log likelihood (CLL) of a
class variable, given the feature variables, were higher than those obtained by
maximizing the marginal likelihood (ML). However, differences between the
performances of the two scores in the earlier studies may be attributed to the
fact that they used approximate learning algorithms, not exact ones. This paper
compares the classification accuracies of BNs with approximate learning using
CLL to those with exact learning using ML. The results demonstrate that the
classification accuracies of BNs obtained by maximizing the ML are higher than
those obtained by maximizing the CLL for large data. However, the results also
demonstrate that the classification accuracies of exact learning BNs using the
ML are much worse than those of other methods when the sample size is small and
the class variable has numerous parents. To resolve the problem, we propose an
exact learning augmented naive Bayes classifier (ANB), which ensures a class
variable with no parents. The proposed method is guaranteed to asymptotically
estimate the identical class posterior to that of the exactly learned BN.
Comparison experiments demonstrated the superior performance of the proposed
method.Comment: 29 page