ArticleFrontiers in microbiology2026
IMicAP: an intelligent microbial application platform for knowledge-driven microbiome research.
Article in Frontiers in microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors.
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Abstract
Background: Microorganisms play a pivotal role in health and disease, yet microbial research is hindered by fragmented data, heterogeneous knowledge representation, and a lack of integrated analytical tools. Existing databases and knowledge graphs often focus narrowly on genomic information, leaving critical metabolomic and literature-based associations underdeveloped. Results: We developed the Intelligent Microbial Application Platform (IMicAP), an AI-driven, open-access web platform that integrates 12 microbial databases and over 35 million articles. IMicAP delivers 5 core modules: (1) Knowledge Query module for rapid data retrieval; (2) Knowledge Graph module for multi-hop reasoning and hypothesis generation; (3) Intelligent Question-Answering module for professional natural language dialogue in the field of microbiology; (4) Genome Browser module for genomic visualization; and (5) 16S rRNA Sequencing Analysis module for zero-code microbial bioinformatics analysis. Using curated microbe-disease and microbe-small molecule relations extracted with biomedical language models, IMicAP enables context-aware exploration of microbial relations. Conclusion: IMicAP (available at: https://www.imicap.com:8443) is the first comprehensive, open-access, AI-powered microbial platform designed to bridge the gap between fragmented microbial data and actionable biological insights. It directly addresses 4 key limitations in existing resources: (1) Fragmented analysis without integrated workflows; (2) Narrow disease scope covering only one or two disease types; (3) Incomplete knowledge from restricted data sources; and (4) neglect of metabolomic information. IMicAP serves as a versatile resource for advancing microbiome research and translational discoveries. Future development focus on expanding microbial coverage, enhancing reasoning scalability, and evolving toward a dual knowledge-and-data-driven architecture to further support mechanistic discovery in microbial systems biology.
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