SPFuseRanker: A Multi-Importance Score Fusion Framework for Core Microbiome Identification in Metagenomic Data

Clinical metagenomic data are typically high-dimensional, sparse, and zero-inflated, and are often characterized by limited sample sizes and measurement noise. In addition, different feature-importance methods may produce inconsistent taxon rankings, which limits the stability of single-method feature selection and complicates the identification of candidate core microbiome members. To address this issue, we propose SPFuseRanker, a score-fusion-based ranking framework for integrating multiple microbial importance measures in metagenomic data. The method constructs a consensus score vector by combining heterogeneous importance signals, including statistical tests, correlation analysis, univariate classification performance, and tree-based feature importance. A top-weighted distance function based on Softmax normalization is introduced to emphasize highly ranked taxa during the fusion process. The optimization of the fused score vector is formulated as a minimum-distance problem and solved using a genetic algorithm (GA). We evaluate the proposed method using both synthetic simulations and a real-world systemic lupus erythematosus (SLE) gut microbiome dataset. Experimental results show that SPFuseRanker achieves more stable ranking performance compared with several representative rank aggregation and score fusion methods, particularly in terms of ranking consistency and robustness under noise. In addition, the selected candidate microbial taxa demonstrate improved predictive performance in disease classification tasks, suggesting their potential relevance to SLE-associated microbial signatures. Overall, SPFuseRanker provides a practical framework for integrating multi-source importance information and may serve as a useful tool for candidate core microbiome identification in metagenomic studies.