MAPA: A Semantic Network Framework for Functional Module Discovery and Interpretation in Multi-Omics Data

Multi-omics technologies generate high-dimensional molecular signatures that provide unprecedented opportunities to uncover biological mechanisms. However, translating complex molecular alterations into coherent and interpretable functional insights remains a major challenge. Existing module discovery methods can identify groups of related features, but often lack direct biological interpretability, whereas pathway-based approaches frequently yield redundant results that complicate interpretation. Here, we present MAPA (Modular Analysis and Phenotype-informed Annotation using large language models [LLMs]), a semantic-biological network framework for functional module discovery and interpretation in multi-omics data. MAPA integrates molecular interactions and pathway-level functional context into a unified semantic-biological network, and applies random walk with restart to quantify global functional relatedness among molecules and pathways for coherent module discovery across omics layers. MAPA further incorporates LLM-assisted interpretation with retrieval-augmented generation (RAG) to produce structured, literature-informed module interpretation. Benchmarking against existing approaches shows that MAPA achieves superior module reconstruction and expert-aligned functional interpretation. Applied to aging-related multi-omics datasets, MAPA reveals biologically coherent modules and biological insights that are difficult to obtain from conventional pathway analyses alone. MAPA provides a generalizable framework for organizing fragmented and heterogeneous molecular features into functional modules and comprehensive interpretations.