Bayesian networks established functional differences between breast cancer subtypes

Abstract

Breast cancer is a heterogeneous disease. In clinical practice, tumors are classified as hormonal receptor positive, Her2 positive and triple negative tumors. In previous works, our group defined a new hormonal receptor positive subgroup, the TN-like subtype, which had a prognosis and a molecular profile more similar to triple negative tumors. In this study, proteomics and Bayesian networks were used to characterize protein relationships in 96 breast tumor samples. Components obtained by these methods had a clear functional structure. The analysis of these components suggested differences in processes such as mitochondrial function or extracellular matrix between breast cancer subtypes, including our new defined subtype TN-like. In addition, one of the components, mainly related with extracellular matrix processes, had prognostic value in this cohort. Functional approaches allow to build hypotheses about regulatory mechanisms and to establish new relationships among proteins in the breast cancer context.

Predictive model of positive surgical margins after radical prostatectomy based on Bayesian network analysis

“…For example, the dependent variables included in the logistic regression model should follow an exponential distribution, while the establishment of regression models is primarily based on statistically significant factors. Bayesian network analysis, as a machine learning method combining probability theory with graph theory, can be used to analyze a problem structure combined with conditional probability, and it is often implemented in the establishment of models such as disease prediction ( 17 ), treatment effect evaluation ( 7 ), and diagnosis and treatment decision making ( 18 )—importantly, it displays acceptable efficiency. In this study, analyses by naive Bayesian network, TAN Bayesian network, and a nomogram model were exploited to predict positive surgical margins after radical prostatectomy, with the respective ROC curves for the Bayesian network and nomogram model constructed and the AUCs calculated to evaluate the superiority or inferiority of the models.…”

Utilization of model-agnostic explainable artificial intelligence frameworks in oncology: a narrative review

“…That being said, there are already existing methods and algorithms in the ML/AI toolkit that are more explainable and interpretable by design, such as probabilistic causal modeling, various Bayesian methodologies and fast/ oblique decision trees. Adaptation of such techniques to the oncology spaces is an emerging trend (57)(58)(59)(60)(61). Notably, Bayesian network modeling aims to construct and visualize graphical probabilistic/causal multiscale models from the "flat" multivariate data; while outside of the scope of this communication, there is significant recent body of work in the Bayesian networks in oncology space, numbering ~100 publications per annum in the 2020s.…”

Integration of artificial intelligence in lung cancer: Rise of the machine

“…These include “simple” decision trees, regression models such as least absolute shrinkage and selection operator (LASSO), and Bayesian probabilistic causal networks, though these are not applicable to all clinical problems/models. 89 , 90 , 91 , 92 , 93 , 94 , 95 …”