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

Authors

Yifei Ge, Feifan Zhang, Yijiang Liu, Chao Jiang, Peng Gao, Nguan Soon Tan, Sai Zhang, Yuchen Shen, Qianyi Zhou, Xin Zhou, Xiao Wang, Fangqing Zhao, Chuchu Wang, Xiaotao Shen

Published in

Advanced science (Weinheim, Baden-Wurttemberg, Germany). Pages e77774. Sep 16, 2026. Epub Sep 16, 2026.

Abstract

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.

PMID:
42750316
Bibliographic data and abstract were imported from PubMed on 17 Sep 2026.

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