近年來，總體基因體學利用 Hi-C 定序數據從複雜的微生物群落中收復總體基因體組裝基因體 (MAGs)。本研究進一步驗證了先前提出的假設，即可以通過網路相關的指標來預測 MAG 質量。我們深入分析了總體基因體Hi-C 接觸圖，提取了額外的網路屬性，並整合了來自群聚基因組的生物信息，以提升預測表現。這種網路與生物屬性相結合的特徵在機器學習模型中的應用，不僅增強了 MAG 質量預測，還提供了對微生物群落動態的見解。

Recent advancements in Metagenomics leverage Hi-C sequencing data to recover Metagenome Assembled Genomes (MAGs) from complex microbial communities. This research advances MAG quality prediction by building upon previous hypotheses that network-based metrics could be used to predict MAG quality. Deeper analysis of metagenomic HI-C contact maps extracts additional network properties and integrates biological information from the clustered genomes, enhancing predictive performance. This combination of network and biological properties used as features in Machine Learning Models, enhances MAG quality prediction and offers insights into microbial community dynamics.

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