BayeSuites: An open web framework for massive Bayesian networks focused on neuroscience

Michiels, Mario; Larrañaga, Pedro; Bielza, Concha

doi:10.1016/j.neucom.2020.11.066

Cited by 14 publications

(8 citation statements)

References 36 publications

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“…For this type of probabilistic graphical model, we use BayeSuites (embedded within NeuroSuites), a web framework developed in our lab for massive Bayesian networks focused on neuroscience (Michiels et al, 2021). Once the desired variables have been selected, we can learn the structure graph and the parameters from a dataset by selecting a structure and parameter learning algorithm.…”

Section: Bayesian Networkmentioning

confidence: 99%

RETRACTED: NeuroSuites: An online platform for running neuroscience, statistical, and machine learning tools

Moreno-Rodríguez

Larrañaga²,

Bielza³

2023

Front. Neuroinform.

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Nowadays, an enormous amount of high dimensional data is available in the field of neuroscience. Handling these data is complex and requires the use of efficient tools to transform them into useful knowledge. In this work we present NeuroSuites, an easy-access web platform with its own architecture. We compare our platform with other software currently available, highlighting its main strengths. Thanks to its defined architecture, it is able to handle large-scale problems common in some neuroscience fields. NeuroSuites has different neuroscience-oriented applications and tools to integrate statistical data analysis and machine learning algorithms commonly used in this field. As future work, we want to further expand the list of available software tools as well as improve the platform interface according to user demands.

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Section: Bayesian Networkmentioning

confidence: 99%

RETRACTED: NeuroSuites: An online platform for running neuroscience, statistical, and machine learning tools

Moreno-Rodríguez

Larrañaga²,

Bielza³

2023

Front. Neuroinform.

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“…For this reason, there is a growing interest to develop tools that can model how fish microbiomes and their hosts interact under variable farming conditions. Along these lines, Bayesian networks (BN) and structure learning [ 10 , 11 , 12 ] may be especially useful due to their capacity to infer directional relationships in microbial communities [ 13 , 14 ]. Certainly, BNs are probabilistic graphical models based on the Bayes Theorem that represent and evaluate the conditional dependencies among a set of variables via directed acyclic graphs (DAG).…”

Section: Introductionmentioning

confidence: 99%

SAMBA: Structure-Learning of Aquaculture Microbiomes Using a Bayesian Approach

Soriano,

Hafez,

Naya-Català

et al. 2023

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Gut microbiomes of fish species consist of thousands of bacterial taxa that interact among each other, their environment, and the host. These complex networks of interactions are regulated by a diverse range of factors, yet little is known about the hierarchy of these interactions. Here, we introduce SAMBA (Structure-Learning of Aquaculture Microbiomes using a Bayesian Approach), a computational tool that uses a unified Bayesian network approach to model the network structure of fish gut microbiomes and their interactions with biotic and abiotic variables associated with typical aquaculture systems. SAMBA accepts input data on microbial abundance from 16S rRNA amplicons as well as continuous and categorical information from distinct farming conditions. From this, SAMBA can create and train a network model scenario that can be used to (i) infer information of how specific farming conditions influence the diversity of the gut microbiome or pan-microbiome, and (ii) predict how the diversity and functional profile of that microbiome would change under other variable conditions. SAMBA also allows the user to visualize, manage, edit, and export the acyclic graph of the modelled network. Our study presents examples and test results of Bayesian network scenarios created by SAMBA using data from a microbial synthetic community, and the pan-microbiome of gilthead sea bream (Sparus aurata) in different feeding trials. It is worth noting that the usage of SAMBA is not limited to aquaculture systems as it can be used for modelling microbiome–host network relationships of any vertebrate organism, including humans, in any system and/or ecosystem.

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“…In the current analysis, the relationships and structure of data is understood using a Dynamic Bayesian Network. A Bayesian Network (BN) is a compact representation of statistical dependencies among variables (Neapolitan, 2004 ; Koller and Friedman, 2009 ; Bielza and Larrañaga, 2014 ; Michiels et al, 2021 ). BNs are probabilistic models defined by a Directed Acyclic Graph (DAG) and conditional probabilities tables (CPT) representing the probabilistic dependence over signals.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Network Dynamical Model of Rat Brains During Acute Ictogenesis

Tsukahara

Júnior

Barth

et al. 2022

Front. Neural Circuits

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Epilepsy is one of the most common neurological disorders worldwide. Recent findings suggest that the brain is a complex system composed of a network of neurons, and seizure is considered an emergent property resulting from its interactions. Based on this perspective, network physiology has emerged as a promising approach to explore how brain areas coordinate, synchronize and integrate their dynamics, both under perfect health and critical illness conditions. Therefore, the objective of this paper is to present an application of (Dynamic) Bayesian Networks (DBN) to model Local Field Potentials (LFP) data on rats induced to epileptic seizures based on the number of arcs found using threshold analytics. Results showed that DBN analysis captured the dynamic nature of brain connectivity across ictogenesis and a significant correlation with neurobiology derived from pioneering studies employing techniques of pharmacological manipulation, lesion, and modern optogenetics. The arcs evaluated under the proposed approach achieved consistent results based on previous literature, in addition to demonstrating robustness regarding functional connectivity analysis. Moreover, it provided fascinating and novel insights, such as discontinuity between forelimb clonus and generalized tonic-clonic seizure (GTCS) dynamics. Thus, DBN coupled with threshold analytics may be an excellent tool for investigating brain circuitry and their dynamical interplay, both in homeostasis and dysfunction conditions.

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BayeSuites: An open web framework for massive Bayesian networks focused on neuroscience

Cited by 14 publications

References 36 publications

RETRACTED: NeuroSuites: An online platform for running neuroscience, statistical, and machine learning tools

RETRACTED: NeuroSuites: An online platform for running neuroscience, statistical, and machine learning tools

SAMBA: Structure-Learning of Aquaculture Microbiomes Using a Bayesian Approach

Data-Driven Network Dynamical Model of Rat Brains During Acute Ictogenesis

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