3,727 research outputs found
Granger causality vs. dynamic Bayesian network inference: a comparative study
Background
In computational biology, one often faces the problem of deriving the causal relationship among different elements such as genes, proteins, metabolites, neurons and so on, based upon multi-dimensional temporal data. Currently, there are two common approaches used to explore the network structure among elements. One is the Granger causality approach, and the other is the dynamic Bayesian network inference approach. Both have at least a few thousand publications reported in the literature. A key issue is to choose which approach is used to tackle the data, in particular when they give rise to contradictory results.
Results
In this paper, we provide an answer by focusing on a systematic and computationally intensive comparison between the two approaches on both synthesized and experimental data. For synthesized data, a critical point of the data length is found: the dynamic Bayesian network outperforms the Granger causality approach when the data length is short, and vice versa. We then test our results in experimental data of short length which is a common scenario in current biological experiments: it is again confirmed that the dynamic Bayesian network works better.
Conclusion
When the data size is short, the dynamic Bayesian network inference performs better than the Granger causality approach; otherwise the Granger causality approach is better
Brain connectivity during the processing of nouns and verbs: a dynamic Bayesian network analysis
Dynamic Bayesian network was used to study the connections among the brain regions activated during processing of nouns and verbs. Under simplifying assumptions, we arrived at a dynamic Bayesian network learning algorithm with reduced time complexity, which allowed us to test all possible connectivity models exhaustively and choose the best model based on the Bayesian information criterion (BIC) score. We found a posterior to anterior flow of processing of both nouns and verbs. The left medial frontal gyrus was found to play an important role in the network. For verb processing, strong involvements of motor cortex and cerebellum were found.published_or_final_versio
Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model
Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships
Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model
Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships
A fuzzy dynamic bayesian network-based situation assessment approach
Situation awareness (SA), a state in the mind of a human, is essential to conduct decision-making activities. It is about the perception of the elements in the environment, the comprehension of their meaning, and the projection of their status in the near future. Two decades of investigation and analysis of accidents have showed that SA was behind of many serious large-scale technological systems' accidents. This emphasizes the importance of SA support systems development for complex and dynamic environments. This paper presents a fuzzy dynamic Bayesian network-based situation assessment approach to support the operators in decision making process in hazardous situations. The approach includes a dynamic Bayesian network-based situational network to model the hazardous situations where the existence of the situations can be inferred by sensor observations through the SCADA monitoring system using a fuzzy quantizer method. In addition to generate the assessment result, a fuzzy risk estimation method is proposed to show the risk level of situations. Ultimately a hazardous environment from U.S. Chemical Safety Board investigation reports has been used to illustrate the application of proposed approach. © 2013 IEEE
BANSAC: A dynamic BAyesian Network for adaptive SAmple Consensus
RANSAC-based algorithms are the standard techniques for robust estimation in
computer vision. These algorithms are iterative and computationally expensive;
they alternate between random sampling of data, computing hypotheses, and
running inlier counting. Many authors tried different approaches to improve
efficiency. One of the major improvements is having a guided sampling, letting
the RANSAC cycle stop sooner. This paper presents a new adaptive sampling
process for RANSAC. Previous methods either assume no prior information about
the inlier/outlier classification of data points or use some previously
computed scores in the sampling. In this paper, we derive a dynamic Bayesian
network that updates individual data points' inlier scores while iterating
RANSAC. At each iteration, we apply weighted sampling using the updated scores.
Our method works with or without prior data point scorings. In addition, we use
the updated inlier/outlier scoring for deriving a new stopping criterion for
the RANSAC loop. We test our method in multiple real-world datasets for several
applications and obtain state-of-the-art results. Our method outperforms the
baselines in accuracy while needing less computational time.Comment: ICCV 2023 pape
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