%0 Journal Article
%T Bayesian Network Modeling for Gene Regulatory System Analysis in Precision Biotechnology
%A Angel-Javier Quispe-Carita
%A Bernabé Canqui-Flores
%A Juan-Carlos Juarez-Vargas
%A José-Pánfilo Tito-Lipa
%A Juan-Reynaldo Paredes-Quispe
%A Milton-Antonio López-Cueva
%A Leonel Coyla-Idme
%J Journal of Biochemical Technology
%@ 0974-2328
%D 2026
%V 17
%N 1
%R 10.51847/x02xsQnRtf
%P 157-165
%X Bayesian network modeling offers a principled strategy for reconstructing regulatory dependencies from high-dimensional transcriptomic data, particularly when biological interpretation requires more than pairwise association. This study aimed to infer a cancer-subtype-specific gene regulatory network from paired tumor and matched normal RNA-seq profiles and to identify regulatory hubs with potential relevance for precision biotechnology. Tumor and matched normal transcriptomes were preprocessed through expression normalization, low-abundance gene filtering, variance-based feature selection, and discretization for probabilistic structure learning. Two Bayesian network structure learning strategies were compared: Hill-Climbing as a score-based algorithm and PC-stable as a constraint-based algorithm. Known transcription factor–target interactions were incorporated as structural priors to improve biological plausibility. The final consensus Bayesian network contained 512 genes and 1,847 directed edges after bootstrap stability filtering. Hill-Climbing produced denser but more reproducible local regulatory neighborhoods, whereas PC-stable produced a sparser graph enriched for high-confidence conditional dependencies. A total of 15 hub genes showed significant association with disease-specific survival. The inferred regulatory architecture recovered several established oncogenic signaling patterns and nominated additional candidate regulators not captured by differential expression alone. Network modules were enriched for cell-cycle progression, epithelial–mesenchymal transition, immune regulation, chromatin remodeling, and DNA-damage response pathways. The main limitations were the static nature of the inferred network, the use of a single cancer subtype, and the moderate sample size available for paired tumor-normal analysis. Nevertheless, the study demonstrates that rigorously validated Bayesian network inference can convert transcriptomic profiles into clinically interpretable regulatory systems for precision biotechnology.
%U https://jbiochemtech.com/article/bayesian-network-modeling-for-gene-regulatory-system-analysis-in-precision-biotechnology-5yd30bxegd86vzx