A decomposition structure learning algorithm in Bayesian network based on a two-stage combination method

Guo, Huiping; Li, Hongru

doi:10.1007/s40747-021-00623-3

Cited by 4 publications

(3 citation statements)

References 36 publications

(70 reference statements)

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“…In this study, the dataset has been divided into two categories, i.e., vaccinated and unvaccinated. The Microarray datasets have been utilized for inscribing Gene Expression Omnibus of the National Centre of Biotechnology to extract the raw gene of "SARS-CoV" [29]. In addition, the T-cells epitope Affymetrix microarray dataset was gathered for different variants under vaccination and nonvaccination phases.…”

Section: Datasetmentioning

confidence: 99%

Prediction of the SARS-CoV-2 Derived T-Cell Epitopes’ Response Against COVID Variants

Tahir¹,

Khan²,

Ahmed³

et al. 2023

Computers, Materials &Amp; Continua

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The COVID-19 outbreak began in December 2019 and was declared a global health emergency by the World Health Organization. The four most dominating variants are Beta, Gamma, Delta, and Omicron. After the administration of vaccine doses, an eminent decline in new cases has been observed. The COVID-19 vaccine induces neutralizing antibodies and T-cells in our bodies. However, strong variants like Delta and Omicron tend to escape these neutralizing antibodies elicited by COVID-19 vaccination. Therefore, it is indispensable to study, analyze and most importantly, predict the response of SARS-CoV-2-derived t-cell epitopes against Covid variants in vaccinated and unvaccinated persons. In this regard, machine learning can be effectively utilized for predicting the response of COVID-derived t-cell epitopes. In this study, prediction of T-cells Epitopes' response was conducted for vaccinated and unvaccinated people for Beta, Gamma, Delta, and Omicron variants. The dataset was divided into two classes, i.e., vaccinated and unvaccinated, and the predicted response of T-cell Epitopes was divided into three categories, i.e., Strong, Impaired, and Over-activated. For the aforementioned prediction purposes, a self-proposed Bayesian neural network has been designed by combining variational inference and flow normalization optimizers. Furthermore, the Hidden Markov Model has also been trained on the same dataset to compare the results of the self-proposed Bayesian neural network with this state-of-the-art statistical approach. Extensive experimentation and results demonstrate the efficacy of the proposed network in terms of accurate prediction and reduced error.

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Section: Datasetmentioning

confidence: 99%

Prediction of the SARS-CoV-2 Derived T-Cell Epitopes’ Response Against COVID Variants

Tahir¹,

Khan²,

Ahmed³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…This article uses the Bayesian information criterion (BIC) score as the standard to measure the quality of the structure [28]. The BIC score function consists of two parts: the log-likelihood function that measures the degree to which the candidate structure matches the sample data, and the penalty related to the dimensionality of the model and the size of the dataset.…”

Section: Bayesian Information Criterion Scoring Functionmentioning

confidence: 99%

Self-Awakened Particle Swarm Optimization BN Structure Learning Algorithm Based on Search Space Constraint

Liu,

Li,

Zhang

et al. 2023

Computers, Materials &Amp; Continua

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To obtain the optimal Bayesian network (BN) structure, researchers often use the hybrid learning algorithm that combines the constraint-based (CB) method and the score-and-search (SS) method. This hybrid method has the problem that the search efficiency could be improved due to the ample search space. The search process quickly falls into the local optimal solution, unable to obtain the global optimal. Based on this, the Particle Swarm Optimization (PSO) algorithm based on the search space constraint process is proposed. In the first stage, the method uses dynamic adjustment factors to constrain the structure search space and enrich the diversity of the initial particles. In the second stage, the update mechanism is redefined, so that each step of the update process is consistent with the current structure which forms a one-to-one correspondence. At the same time, the "self-awakened" mechanism is added to prevent precocious particles from being part of the best. After the fitness value of the particle converges prematurely, the activation operation makes the particles jump out of the local optimal values to prevent the algorithm from converging too quickly into the local optimum. Finally, the standard network dataset was compared with other algorithms. The experimental results showed that the algorithm could find the optimal solution at a small number of iterations and a more accurate network structure to verify the algorithm's effectiveness.

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“…Dai et al [16] improved the sketching performance of the algorithm by improving the Markov blanket search method and proposed a subgraph decomposition method based on the k-path node centrality. Guo et al [17] proposed a two-stage reunion method based on the heuristic search for the reunion step.…”

Section: Introductionmentioning

confidence: 99%

Decomposition-Based Bayesian Network Structure Learning Algorithm for Abnormity Diagnosis Model for Coal Mill Process

Chang

Liu²,

Kang³

et al. 2022

Electronics

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In the structure learning of the large-scale Bayesian network (BN) model for the coal mill process, taking the view of the problem that the decomposition-based method cannot guarantee the sufficient learning of abnormal state node neighborhood in the diagnosis model, this paper proposes a new BN structure learning method based on decomposition. Firstly, a sketch is constructed based on an improved Markov blanket discovery algorithm and edge thickening and thinning. Second, the node centrality of k-path is used to search the important nodes, and the subgraph decomposition is realized by extracting these important nodes and their neighborhoods from the sketch. Then, through the targeted design of subgraph de-duplication, subgraph learning, and subgraph reorganization methods, the learning of large-scale BN is realized. This method is applied to public data sets, and its advantages and disadvantages are analyzed by comparing them with other methods. The advantage of the BN structure learning method of the abnormal condition diagnosis model is further verified by applying the method to the coal mill process, which is consistent with the original design intention.

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A decomposition structure learning algorithm in Bayesian network based on a two-stage combination method

Cited by 4 publications

References 36 publications

Prediction of the SARS-CoV-2 Derived T-Cell Epitopes’ Response Against COVID Variants

Prediction of the SARS-CoV-2 Derived T-Cell Epitopes’ Response Against COVID Variants

Self-Awakened Particle Swarm Optimization BN Structure Learning Algorithm Based on Search Space Constraint

Decomposition-Based Bayesian Network Structure Learning Algorithm for Abnormity Diagnosis Model for Coal Mill Process

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