Fig. 4

Bayesian network analysis of urine biomarker levels in relation to clinical and pathological indices in a cohort of LN patients. The same urine biomarker data plotted in Fig. 1, and the clinical features of the study subjects were subjected to Bayesian network analysis using BayesiaLab. The network shown was constructed in an unsupervised manner, using the EQ algorithm and a structural coefficient of 0.4. The circular nodes that make up the Bayesian Network represent the variables of interest, including urine biomarkers (purple-colored), histological or clinical indices (green-colored), demographic data (yellow-colored), and disease status (active LN versus inactive disease versus no disease) (colored brown). The size of each node denotes the “node force,” which is related to its impact on other nodes in the network, based on conditional probabilities. The links (arcs) that interconnect the nodes represent informational or causal dependencies among the variables, including the correlation coefficients between neighboring nodes, as listed. Blue and red links represent positive and negative correlation, respectively, with the thickness of the link being proportional to the correlation coefficient