scholarly journals Adaptive learning of dynamic Bayesian networks with changing structures by detecting geometric structures of time series

2008 ◽  
Vol 17 (2) ◽  
pp. 263-263
Author(s):  
Kaijun Wang ◽  
Junying Zhang ◽  
Fengshan Shen ◽  
Lingfeng Shi
Keyword(s):  
Time Series ◽  
Bayesian Networks ◽  
Adaptive Learning ◽  
Dynamic Bayesian Networks ◽  
Geometric Structures

Time Series: Dynamic Bayesian Networks

2021 ◽  
pp. 79-90
Author(s):  
Marco Scutari ◽  
Jean-Baptiste Denis
Keyword(s):  
Time Series ◽  
Bayesian Networks ◽  
Dynamic Bayesian Networks

Cancer classification from time series microarray data through regulatory Dynamic Bayesian Networks

2020 ◽  
Vol 116 ◽  
pp. 103577 ◽  
Author(s):  
Konstantina Kourou ◽  
George Rigas ◽  
Costas Papaloukas ◽  
Michalis Mitsis ◽  
Dimitrios I. Fotiadis
Keyword(s):  
Time Series ◽  
Bayesian Networks ◽  
Microarray Data ◽  
Dynamic Bayesian Networks ◽  
Cancer Classification ◽  
Time Series Microarray

scholarly journals Dynamic Bayesian networks for integrating multi-omics time-series microbiome data

2019 ◽  
Author(s):  
Daniel Ruiz-Perez ◽  
Jose Lugo-Martinez ◽  
Natalia Bourguignon ◽  
Kalai Mathee ◽  
Betiana Lerner ◽  
...  
Keyword(s):  
Time Series ◽  
Bayesian Networks ◽  
Joint Modeling ◽  
Dynamic Bayesian Networks ◽  
Series Data ◽  
Omics Data ◽  
Computational Pipeline ◽  
Metabolomics Data ◽  
Experimental Validations ◽  
Microbiome Data

ABSTRACTA key challenge in the analysis of longitudinal microbiome data is the inference of temporal interactions between microbial taxa, their genes, the metabolites they consume and produce, and host genes. To address these challenges we developed a computational pipeline, PALM, that first aligns multi-omics data and then uses dynamic Bayesian networks (DBNs) to reconstruct a unified model. Our approach overcomes differences in sampling and progression rates, utilizes a biologically-inspired multi-omic framework, reduces the large number of entities and parameters in the DBNs, and validates the learned network. Applying PALM to data collected from inflammatory bowel disease patients, we show that it accurately identifies known and novel interactions. Targeted experimental validations further support a number of the predicted novel metabolite-taxa interactions.Source code and data will be freely available after publication under the MIT Open Source license agreement on our GitHub page.IMPORTANCEWhile a number of large consortia are collecting and profiling several different types of microbiome and genomic time series data, very few methods exist for joint modeling of multi-omics data sets. We developed a new computational pipeline, PALM, which uses Dynamic Bayesian Networks (DBNs) and is designed to integrate multi-omics data from longitudinal microbiome studies. When used to integrate sequence, expression, and metabolomics data from microbiome samples along with host expression data, the resulting models identify interactions between taxa, their genes and the metabolites they produce and consume, and their impact on host expression. We tested the models both by using them to predict future changes in microbiome levels, and by comparing the learned interactions to known interactions in the literature. Finally, we performed experimental validations for a few of the predicted interactions to demonstrate the ability of the method to identify novel relationships and their impact.

scholarly journals Identifying Latent Variables in Dynamic Bayesian Networks with Bootstrapping Applied to Type 2 Diabetes Complication Prediction

2020 ◽  
Author(s):  
Leila Yousefi ◽  
Mashael Al-Luhaybi ◽  
Lucia Sacchi ◽  
Luca Chiovato ◽  
Allan Tucker
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Time Series ◽  
Bayesian Networks ◽  
Latent Variables ◽  
Dynamic Bayesian Networks ◽  
Patient Specific ◽  
Probabilistic Networks ◽  
Bootstrapping Method

Abstract Background: Type 2 Diabetes is a chronic disease with an onset that is commonly associated with multiple life-threatening comorbidities (complications). Early prediction of diabetic complications while discovering the behaviour of associated aggressive risk factors can reduce the patients’ suffering time. Therefore, models of the time series diabetic data (which are often imbalanced, incomplete and involve complex interactions) are needed to better manage diabetic complications.Aims: The aim of this work is to both deals with imbalanced clinical data using a bootstrapping approach, whilst determining the precise position of latent variables within probabilistic networks generated from the observations. The main motivation behind this paper is to stratify patient groups by means of latent variables to discover how complications in diabetes interact.Methods: We propose a time series bootstrapping method for building Dynamic Bayesian Networks that includes hidden/latent variables, applied to a case for predicting T2DM complications. A combination of the IC* algorithm on time series bootstrapped data is utilised to identify the latent variables within a Bayesian model. Then, an exploration of inference methods assessed the influences of these latent variables.Results: Our promising findings show how this targeted use of latent variables improves prediction accuracy, specificity, and sensitivity over standard approaches as well as aiding the understanding of relationships between these latent variables and disease complications/risk factors. The contribution of this paper compared to the previous papers in which time series bootstrapping is used for re-balancing the data and providing confidence in the prediction results.Conclusion: Our results showed that our re-balancing approach by the use of Time Series bootstrapping method for an unequal number of time series visits demonstrated an improvement in the prediction performance. Additionally, the most highlighted contribution of this paper gained insight by interpreting the latent states (looking at the associated distributions of complications), which led to a better understanding of risk factors and patient-specific interventions: here the fact that the latent variable demonstrated that a patient falls into a sub-group that is hypertensive but not suffering from retinopathy.

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