A survey on Bayesian network structure learning from data

Scanagatta, Mauro; Salmerón, Antonio; Stella, Fabio

doi:10.1007/s13748-019-00194-y

Cited by 176 publications

(85 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Parameter estimation involves learning the conditional probability distributions based on the data given a known network structure (Ji, Xia, & Meng, 2015). Structural learning involves learning both the network structure and the parameters by combining a statistical criterion with an algorithm that determines how to apply that criterion to the data (Scanagatta, Salmeron, & Stella, 2019).…”

Section: Analytical Approaches For Molecular Networkmentioning

confidence: 99%

Molecular networks in Network Medicine: Development and applications

Silverman

Schmidt

Anastasiadou

et al. 2020

WIREs Mechanisms of Disease

152

118

View full text Add to dashboard Cite

Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein-protein interaction networks, correlation-based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseases.

show abstract

Section: Analytical Approaches For Molecular Networkmentioning

confidence: 99%

Molecular networks in Network Medicine: Development and applications

Silverman

Schmidt

Anastasiadou

et al. 2020

WIREs Mechanisms of Disease

152

118

View full text Add to dashboard Cite

show abstract

“…There are a number of established techniques available to learn both the parameters and the structure [18]. Algorithms for learning a BN structure from data have two components: a scoring metric and a search procedure.…”

Section: A Misper-bayes Model Developmentmentioning

confidence: 99%

Misper-Bayes: A Bayesian Network Model for Missing Person Investigations

et al. 2021

View full text Add to dashboard Cite

Bayesian Networks are probabilistic graph models that can be used for classification, prediction, diagnosis and parameter learning. Probabilities can be inferred from the models and missing values can be imputed, based on probability theory. Missing person cases place a strain on the already overstretched resources of Police Forces. Such cases predominantly come from at risk groups such as children in care and people suffering from depression or dementia. Current approaches for dealing with such cases are manual and rely upon empirical studies and domain knowledge. This paper proposes the use of a Bayesian Network model, which can be used to predict the likely location of a missing person (misper) for a number of at risk groups. The model is evaluated using a set of misper cases and results compare very favourably with those of the manual processes currently used by UK Police forces. The novel approach described provides both a theoretical foundation and a practical framework for the future development of a decision support system. In addition to the model, a contribution is made through guidelines, which recount experiences in learning a Bayesian Network from data.

show abstract

“…However, a rigorous causal analysis of such data, whose collection on hundreds or thousands of subjects is now feasible (Maitre et al 2018), would require knowledge on the causal relations between (and possibly within) each data layer, which may, in many situations, be very difficult to attain. Inferring causal structure from data without strong a priori is an expanding field of research (Scanagatta et al 2019;Uusitalo 2007). The approaches initially used have tended to have a complexity increasing at least exponentially with the number of possible nodes in the causal diagram to infer, but alternatives have been recently suggested that may make the problem tractable also in high dimension .…”

Section: Environmental Health Perspectivesmentioning

confidence: 99%

Challenges Raised by Mediation Analysis in a High-Dimension Setting

Blum

Valeri

Ochsenbein

et al. 2020

Environ Health Perspect

View full text Add to dashboard Cite

BACKGROUND: Mediation analysis is used in epidemiology to identify pathways through which exposures influence health. The advent of highthroughput (omics) technologies gives opportunities to perform mediation analysis with a high-dimension pool of covariates. OBJECTIVE: We aimed to highlight some biostatistical issues of this expanding field of high-dimension mediation. DISCUSSION: The mediation techniques used for a single mediator cannot be generalized in a straightforward manner to high-dimension mediation. Causal knowledge on the relation between covariates is required for mediation analysis, and it is expected to be more limited as dimension and system complexity increase. The methods developed in high dimension can be distinguished according to whether mediators are considered separately or as a whole. Methods considering each potential mediator separately do not allow efficient identification of the indirect effects when mutual influences exist among the mediators, which is expected for many biological (e.g., epigenetic) parameters. In this context, methods considering all potential mediators simultaneously, based, for example, on data reduction techniques, are more adapted to the causal inference framework. Their cost is a possible lack of ability to single out the causal mediators. Moreover, the ability of the mediators to predict the outcome can be overestimated, in particular because many machine-learning algorithms are optimized to increase predictive ability rather than their aptitude to make causal inference. Given the lack of overarching validated framework and the generally complex causal structure of high-dimension data, analysis of high-dimension mediation currently requires great caution and effort to incorporate a priori biological knowledge.

show abstract

A survey on Bayesian network structure learning from data

Cited by 176 publications

References 38 publications

Molecular networks in Network Medicine: Development and applications

Molecular networks in Network Medicine: Development and applications

Misper-Bayes: A Bayesian Network Model for Missing Person Investigations

Challenges Raised by Mediation Analysis in a High-Dimension Setting

Contact Info

Product

Resources

About