ArticleGenomics, proteomics & bioinformatics2020
MicroPhenoDB Associates Metagenomic Data with Pathogenic Microbes, Microbial Core Genes, and Human Disease Phenotypes.
Article in Genomics, proteomics & bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Bacterial-based cancer therapy: mechanisms and therapeutic advances.Molecular biomedicine · 2026Review
- MetagenomicKG: a knowledge graph for metagenomic applications.Bioinformatics (Oxford, England) · 2026Article
- Intratumoral microbiota in cancer: molecular mechanism and therapeutic strategies.Molecular biomedicine · 2026Review
- KG-Microbe: Building modular and scalable knowledge graphs for microbiome and microbial sciences.GigaScience · 2026Article
- HGMT: a database of human gut microbiota for tumors and immunotherapy response.Genome biology · 2025Article
- A lightweight single-view contrastive learning hypergraph neural network for food-microbe-disease association prediction.BMC bioinformatics · 2025Article
- Linking dietary fiber to human malady through cumulative profiling of microbiota disturbance.iMeta · 2025Article
- MicrobiomeKG: bridging microbiome research and host health through knowledge graphs.Frontiers in systems biology · 2025Article
- BodyMeta: A comprehensive database for microbes under various pathological and physiological conditions.Computational and structural biotechnology journal · 2025Article
- BGMDB: A curated database linking gut microbiota dysbiosis to brain disorders.Computational and structural biotechnology journal · 2025Article
- MicrobiomeKG: Bridging Microbiome Research and Host Health Through Knowledge Graphs.bioRxiv : the preprint server for biology · 2024Article
- Guild-based approach for mitigating information loss and distortion issues in microbiome analysis.The Journal of clinical investigation · 2024Article
- Identification of microbe-disease signed associations via multi-scale variational graph autoencoder based on signed message propagation.BMC biology · 2024Article
- MetagenomicKG: a knowledge graph for metagenomic applications.bioRxiv : the preprint server for biology · 2024Article
- Predicting microbe-disease association based on graph autoencoder and inductive matrix completion with multi-similarities fusion.Frontiers in microbiology · 2024Article
- A computational model for potential microbe-disease association detection based on improved graph convolutional networks and multi-channel autoencoders.Frontiers in microbiology · 2024Article
- Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance.BMC biology · 2023Article
- A display and analysis platform for gut microbiomes of minority people and phenotypic data in China.Scientific reports · 2023Article
- Achieving pan-microbiome biological insights via the dbBact knowledge base.Nucleic acids research · 2023Article
- Leveraging pre-trained language models for mining microbiome-disease relationships.BMC bioinformatics · 2023Article
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Microbes play important roles in human health and disease. The interaction between microbes and hosts is a reciprocal relationship, which remains largely under-explored. Current computational resources lack manually and consistently curated data to connect metagenomic data to pathogenic microbes, microbial core genes, and disease phenotypes. We developed the MicroPhenoDB database by manually curating and consistently integrating microbe-disease association data. MicroPhenoDB provides 5677 non-redundant associations between 1781 microbes and 542 human disease phenotypes across more than 22 human body sites. MicroPhenoDB also provides 696,934 relationships between 27,277 unique clade-specific core genes and 685 microbes. Disease phenotypes are classified and described using the Experimental Factor Ontology (EFO). A refined score model was developed to prioritize the associations based on evidential metrics. The sequence search option in MicroPhenoDB enables rapid identification of existing pathogenic microbes in samples without running the usual metagenomic data processing and assembly. MicroPhenoDB offers data browsing, searching, and visualization through user-friendly web interfaces and web service application programming interfaces. MicroPhenoDB is the first database platform to detail the relationships between pathogenic microbes, core genes, and disease phenotypes. It will accelerate metagenomic data analysis and assist studies in decoding microbes related to human diseases. MicroPhenoDB is available through http://www.liwzlab.cn/microphenodb and http://lilab2.sysu.edu.cn/microphenodb.
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