Comprehensive review of Bayesian network applications in gastrointestinal cancers

Table 4 Summary of Bayesian network applications in gastric cancer research

Data type	Bayesian network algorithm	Key findings	Ref.
Patient data from the public SEER database; patient data from a hospital cohort	Naïve Bayesian model	The Bayesian network model identified key risk factors, including age, T-stage, N-stage, tumor size, grade, and tumor location, contributing to the prediction of distant metastasis in stage T1 gastric cancer	[37]
LncRNA expression profiles from 375 STAD samples in the TCGA database	Bayesian Lasso-logistic regression	The Bayesian-based approach identified seven lncRNAs, effectively stratified STAD patients by risk, and demonstrated robust prognostic prediction accuracy with AUC values above 0.69 for 1-, 3-, and 5-year survival	[37]
Survival and censorship data from 760 gastric cancer patients	A two-slice temporal Bayesian network model	The Bayesian network improved prediction accuracy, reduced bias, and aligned with classical methods while handling high-dimensional data effectively	[38]
Data from seven randomized clinical trials involving 2655 metastatic gastric cancer patients	Bayesian fixed-effect network meta-analysis model	The Bayesian analysis identified nivolumab as the optimal choice for OS in mGC patients without peritoneal metastases, providing the best balance of efficacy and safety	[39]
Gene expression data from TCGA gastric cancer and metastatic gastric cancer immunotherapy clinical trial datasets	Bayesian semi-nonnegative matrix trifactorization method	The Bayesian method identified clinically relevant pathways associated with molecular subtypes and immunotherapy response, enabling patient stratification and prognosis prediction in independent validation datasets	[40]
LncRNA-miRNA-disease association data, including known associations related to gastric cancer	A Naïve Bayesian Classifier was integrated into a CFNBC	CFNBC demonstrated reliable prediction performance (AUC of 0.8576) and successfully identified potential lncRNA-disease associations for gastric cancer in case studies	[42]
Clinical data from 339 gastric cancer patients	BNN	The BNN outperformed the ANN in predicting survival of gastric cancer patients, with higher sensitivity, specificity, prediction accuracy, and AUCs	[43]
Data from 245 gastric endoscopic submucosal dissections	Naïve Bayesian model	The Bayesian model demonstrated good discriminative power in predicting ESD outcomes, with naïve Bayesian models presenting AUCs of approximately 80% in the derivation cohort and at least 74% in cross-validation for both outcomes	[44]
Data from the structural domain characteristics of the p42.3 protein molecule	Bayesian network model	The study identified the most likely acting pathway for p42.3 in gastric cancer as "S100A11" - RAGE - P38 - MAPK - Microtubule-associated protein - spindle protein - centromere protein - cell proliferation" through Bayesian probability optimizing calculation, which was subsequently validated by biological experiments	[45]
Genome-wide gene expression profiles	Categorical Bayesian networks	The BN approach outperformed benchmark methods and successfully identified disease-specific changes in gene regulation that differentiate cancer types, improving prediction	[46]
Gene expression profile data from gastric cancer patients	A Bayesian Network was constructed using 18 genes selected by multiple logistic regression	The constructed Bayesian Network was very similar to the network from GeneMANIA, indicating the effectiveness of the Bayesian approach in modeling the relationships among genes associated with gastric cancer subtypes	[47]

SEER: Surveillance, Epidemiology and End Results; STAD: Stomach adenocarcinoma; AUC: Area under curve; OS: Overall survival; CFNBC: Collaborative filtering model; BNN: Bayesian neural network; ANN: Artificial neural network; ESD: Endoscopic submucosal dissection.