ReviewFrontiers in computational neuroscience2014
Bayesian networks in neuroscience: a survey.
Review in Frontiers in computational neuroscience, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Modeling interrelationships between health behaviors in overweight breast cancer survivors: Applying Bayesian networks.PloS one · 2018Trial
- Dynamic Bayesian networks for neural information flow: evaluation of continuous and discrete scoring metrics.Journal of computational neuroscience · 2026Article
- The Constrained Disorder Principle: A Paradigm Shift for Accurate Interactome Mapping and Information Analysis in Complex Biological Systems.Bioengineering (Basel, Switzerland) · 2025Review
- Bayesian networks for network inference in biology.Journal of the Royal Society, Interface · 2025Review
- Article
- Changes in facial expressions can distinguish Parkinson's disease via Bayesian inference.Frontiers in neurology · 2025Article
- Exploring the Potential Imaging Biomarkers for Parkinson's Disease Using Machine Learning Approach.Bioengineering (Basel, Switzerland) · 2024Article
- Practical Bayesian Inference in Neuroscience: Or How I Learned to Stop Worrying and Embrace the Distribution.eNeuro · 2024Article
- Correlations reveal the hierarchical organization of biological networks with latent variables.Communications biology · 2024Article
- Practical Bayesian Inference in Neuroscience: Or How I Learned To Stop Worrying and Embrace the Distribution.bioRxiv : the preprint server for biology · 2024Article
- Multi-source multi-modal markers for Bayesian Networks: Application to the extremely preterm born brain.Medical image analysis · 2024Article
- Bayesian Networks in Radiology.Radiology. Artificial intelligence · 2023Review
- CausNet: generational orderings based search for optimal Bayesian networks via dynamic programming with parent set constraints.BMC bioinformatics · 2023Article
- A Bayesian network model for prediction of low or failed fertilization in assisted reproductive technology based on a large clinical real-world data.Reproductive biology and endocrinology : RB&E · 2023Article
- The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review.Frontiers in public health · 2023Article
- Medical prediction from missing data with max-minus negative regularized dropout.Frontiers in neuroscience · 2023Article
- Association of maternal lipid levels with birth weight and cord blood insulin: a Bayesian network analysis.BMJ open · 2022Article
- Through the looking glass: Deep interpretable dynamic directed connectivity in resting fMRI.NeuroImage · 2022Article
- Causal inference on neuroimaging data with Mendelian randomisation.NeuroImage · 2022Review
- Optimization of anesthetic decision-making in ERAS using Bayesian network.Frontiers in medicine · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bayesian networks are a type of probabilistic graphical models lie at the intersection between statistics and machine learning. They have been shown to be powerful tools to encode dependence relationships among the variables of a domain under uncertainty. Thanks to their generality, Bayesian networks can accommodate continuous and discrete variables, as well as temporal processes. In this paper we review Bayesian networks and how they can be learned automatically from data by means of structure learning algorithms. Also, we examine how a user can take advantage of these networks for reasoning by exact or approximate inference algorithms that propagate the given evidence through the graphical structure. Despite their applicability in many fields, they have been little used in neuroscience, where they have focused on specific problems, like functional connectivity analysis from neuroimaging data. Here we survey key research in neuroscience where Bayesian networks have been used with different aims: discover associations between variables, perform probabilistic reasoning over the model, and classify new observations with and without supervision. The networks are learned from data of any kind-morphological, electrophysiological, -omics and neuroimaging-, thereby broadening the scope-molecular, cellular, structural, functional, cognitive and medical- of the brain aspects to be studied.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.