Leveraging Bayesian Networks for Consensus Network Construction and Multi-Method Feature Selection to Decode Disease Prediction

Abstract

Constructing reliable microbiome co-occurrence networks and identifying disease-associated taxa remain major challenges in microbiome research due to variability introduced by different inference algorithms. To overcome these limitations, we present CMIMN, a novel R package that uses a Bayesian network framework based on conditional mutual information to infer robust microbial interaction networks. To further enhance reliability, we construct a consensus microbiome network by integrating results from CMIMN and three widely used methods--SPIEC-EASI, SPRING, and SPARCC. This consensus approach, which overlays and weights edges shared across methods, reduces inconsistencies and provides a more biologically meaningful view of microbial relationships. In addition, we introduce a multi-method framework for identifying disease-associated microbial taxa by combining machine learning and network-based feature selection. Our ML pipeline applies distinct algorithms and identifies key taxa based on their consistent importance across models. Complementing this, we employ two network-based strategies that prioritize taxa based on centrality differences between clean tubers and scab-infected tubers networks, as well as a composite scoring system that ranks nodes using integrated network metrics. Our results show that CMIMN achieves high robustness in network inference, and that the consensus network further improves stability and interpretability. The multi-method feature selection framework enhances confidence in identifying biologically relevant taxa linked to potato common scab disease. Notably, we identify Bacteroidota, WPS-2, and Proteobacteria at the Phylum level; Actinobacteria, AD3, Bacilli, Anaerolineae, and Ktedonobacteria at the Class level; and C0119, Defluviicoccales, Bacteroidales, and Ktedonobacterales at the Order level as key taxa associated with disease status.