BCTI: a Bayesian network-based method for revealing critical transitions in complex biological systems
Yuyan Tong, Renhao Hong, Na Yang, Pei Chen, Hao Peng, Hui Tang, Rui Liu
TL;DR
BCTI is a new method using Bayesian networks to detect critical transitions in biological systems, helping understand disease progression and improve precision medicine.
Contribution
BCTI introduces a novel Bayesian network-based approach that captures dynamic regulatory changes and detects critical states in biological systems.
Findings
BCTI outperformed or matched existing methods in inferring gene regulatory networks and detecting critical states.
The method effectively analyzes time-course and stage-course high-dimensional expression data.
BCTI provides insights into molecular regulation and has strong potential for precision medicine and systems biology.
Abstract
The identification of critical states during disease progression is essential yet challenging for preventing disease deterioration and developing precision therapies. Traditional methods often rely on the dynamic feature of coordinated molecular variation to provide early-warning signals of impending critical transitions. However, these methods typically overlook the causal relationships among variables, potentially limiting their interpretability in uncovering underlying molecular regulatory mechanisms. With the rapid advancement of sequencing technologies and the surge in high-throughput data, we propose Bayesian Critical Transitions Inference (BCTI), inspired by the time-varying nature of gene regulatory networks. BCTI integrates mutual information and structural equation models to qualitatively capture dynamic changes in network topology and quantitatively evaluate system states…
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Taxonomy
TopicsGene Regulatory Network Analysis · Bioinformatics and Genomic Networks · Ecosystem dynamics and resilience