Gene network analysis plays an important role in the understanding of biological systems, including development, disease, and ecology. This study reviews the development of gene network analysis methods, comparing traditional approaches like equation-based and Bayesian methods with newer techniques based on Random Matrix Theory (RMT). Traditional methods often struggle with scalability, sensitivity to noise, and subjective thresholding. In contrast, RMT offers a systematic and reliable way to determine thresholds automatically. Its integration into the Molecular Ecological Network Analysis (MENA) pipeline strengthens gene network construction by reducing noise, minimizing bias, and increasing robustness. This approach improves the understanding of system modularity and dynamics, addressing the limitations of older methods. This bibliography puts in light RMT's potential to transform gene network analysis and expand its applications in biology and ecology.
Gene Network, Random Matrix Theory, Molecular Ecology, Network Analysis