Association of Novel Androgen Receptor Axis-Targeted Therapies With Diarrhea in Patients With Prostate Cancer: A Bayesian Network Analysis

Xiong, Xingyu; Xu, Hang; Wang, Sheng; Liao, Xinyang; Yi, Xian; Jin, Kun; Lei, Haoran; Bai, Shengjiang; Qiu, Shi; Yang, Lu

doi:10.3389/fmed.2021.800823

Cited by 3 publications

(1 citation statement)

References 42 publications

(29 reference statements)

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“…Bayesian Networks (BN), also known as Belief Network or Directed Acyclic Graphical Model (DAGM), consists of a Directed Acyclic Graph (DAG) and the corresponding conditional probability table, the nodes in the directed acyclic graph represent random variables, the directed edges between nodes represent the dependencies relationships between nodes, the direction of the edges is from the parent node to the child node, the two nodes The strength of the relationship between the nodes is expressed by the conditional probability, 6 VOLUME XX, 2017 and the information of the nodes without parent nodes is expressed by the prior probability. In recent years, Bayesian networks have been applied in many fields such as causal analysis [9], artificial intelligence [10], fault diagnosis [11,12], and medical research [13,14]. At present, Bayesian network structure learning has been proved to be an NP (Non-deterministic Polynomial Hard, NP-hard) problem.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Network Structure Learning Algorithm Based on Probabilistic Incremental Analysis and Constraint

Liu

Cui

et al. 2022

IEEE Access

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To address the problem of low efficiency of the existing hill-climbing algorithm in Bayesian network structure learning, this paper proposes a Bayesian network structure learning algorithm based on probabilistic incremental analysis and constraints. The algorithm constructs a suitable measure for representing the degree of node association in Bayesian networks based on the principle of random forest feature extraction; then uses the method to construct the initial Bayesian network structure and constrains the search space by setting a corresponding threshold for the probability increment centered on each node; finally takes the initial Bayesian network as the starting point and learns it by the forbidden hill-climbing search and BIC scoring method to obtain the optimal Bayesian network structure. Experimental results show that the correlation degree measure and mutual information proposed in this paper have an approximate correlation expression effect; compared with other Bayesian network structure learning algorithms of the same type, the method in this paper has a faster operation speed while ensuring the quality of the learned network. The experimental results show that the Bayesian network structure learning algorithm based on probabilistic incremental analysis and constraints is an effective and efficient Bayesian network structure learning algorithm.

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Section: Introductionmentioning

confidence: 99%