Novel human microbe-disease associations inference based on network consistency projection
Sign Up to like & getrecommendations! Published in 2018 at "Scientific Reports"
DOI: 10.1038/s41598-018-26448-8
Abstract: Increasing evidence shows that microbes are closely related to various human diseases. Obtaining a comprehensive and detailed understanding of the relationships between microbes and diseases would not only be beneficial to disease prevention, diagnosis and… read more here.
Keywords: network; disease associations; disease; projection ... See more keywords
Prioritizing Human Microbe-Disease Associations Utilizing a Node-Information-Based Link Propagation Method
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.2972283
Abstract: Growing evidence shows that microbes in human body and body surface play critical roles in the development of many human diseases. Predicting the underlying associations between diseases and microbes is essential for deeply understanding the… read more here.
Keywords: node information; human microbe; microbe disease; disease ... See more keywords
MDADP: A Webserver Integrating Database and Prediction Tools for Microbe-Disease Associations
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Journal of Biomedical and Health Informatics"
DOI: 10.1109/jbhi.2022.3156166
Abstract: More and more evidence has demonstrated that microbiota play important roles in the life processes of the human body. In recent years, various computational methods have been proposed for identifying potentially disease-associated microbes to save… read more here.
Keywords: prediction tools; database; microbe disease; disease associations ... See more keywords
M3HOGAT: A Multi-View Multi-Modal Multi-Scale High-Order Graph Attention Network for Microbe-Disease Association Prediction
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Journal of Biomedical and Health Informatics"
DOI: 10.1109/jbhi.2024.3429128
Abstract: Numerous scientific studies have found a link between diverse microorganisms in the human body and complex human diseases. Because traditional experimental approaches are time-consuming and expensive, using computational methods to identify microbes correlated with diseases… read more here.
Keywords: disease; network; disease association; microbe disease ... See more keywords
Predicting Microbe-disease Association Based on Multiple Similarities and LINE Algorithm.
Sign Up to like & getrecommendations! Published in 2021 at "IEEE/ACM transactions on computational biology and bioinformatics"
DOI: 10.1109/tcbb.2021.3082183
Abstract: Numerous microbes have been found to have vital impacts on human health through affecting biological processes. Therefore, exploring potential associations between microbes and diseases will promote the understanding and diagnosis of diseases. In this study,… read more here.
Keywords: multiple similarities; microbe disease; line; disease ... See more keywords
MGATMDA: Predicting microbe-disease associations via multi-component graph attention network.
Sign Up to like & getrecommendations! Published in 2021 at "IEEE/ACM transactions on computational biology and bioinformatics"
DOI: 10.1109/tcbb.2021.3116318
Abstract: Microbes are parasitic in various human body organs and play significant roles in a wide range of diseases. Identifying microbe-disease associations is conducive to the identification of potential drug targets. Considering the high cost and… read more here.
Keywords: component; attention; microbe disease; graph ... See more keywords
KGNMDA: A Knowledge Graph Neural Network Method for Predicting Microbe-Disease Associations
Sign Up to like & getrecommendations! Published in 2022 at "IEEE/ACM Transactions on Computational Biology and Bioinformatics"
DOI: 10.1109/tcbb.2022.3184362
Abstract: Accumulated studies discovered that various microbes in human bodies were closely related to complex human diseases and could provide new insight into drug development. Multiple computational methods were constructed to predict microbes that were potentially… read more here.
Keywords: knowledge graph; microbe disease; microbes diseases;
Microbe-Disease Association Prediction Using RGCN through Microbe-Drug-Disease Network.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE/ACM transactions on computational biology and bioinformatics"
DOI: 10.1109/tcbb.2023.3247035
Abstract: Accumulating evidence has shown that microbes play significant roles in human health and diseases. Therefore, identifying microbe-disease associations is conducive to disease prevention. In this article, a predictive method called TNRGCN is designed for microbe-disease… read more here.
Keywords: network; disease; microbe drug; microbe disease ... See more keywords
Predicting potential microbe–disease associations based on dual branch graph convolutional network
Sign Up to like & getrecommendations! Published in 2024 at "Journal of Cellular and Molecular Medicine"
DOI: 10.1111/jcmm.18571
Abstract: Studying the association between microbes and diseases not only aids in the prevention and diagnosis of diseases, but also provides crucial theoretical support for new drug development and personalized treatment. Due to the time‐consuming and… read more here.
Keywords: dual branch; disease; disease associations; microbe disease ... See more keywords
BMCMDA: a novel model for predicting human microbe-disease associations via binary matrix completion
Sign Up to like & getrecommendations! Published in 2018 at "BMC Bioinformatics"
DOI: 10.1186/s12859-018-2274-3
Abstract: BackgroundHuman Microbiome Project reveals the significant mutualistic influence between human body and microbes living in it. Such an influence lead to an interesting phenomenon that many noninfectious diseases are closely associated with diverse microbes. However,… read more here.
Keywords: matrix; disease; model; bmcmda ... See more keywords
Mining microbe–disease interactions from literature via a transfer learning model
Sign Up to like & getrecommendations! Published in 2021 at "BMC Bioinformatics"
DOI: 10.1186/s12859-021-04346-7
Abstract: Background Interactions of microbes and diseases are of great importance for biomedical research. However, large-scale of microbe–disease interactions are hidden in the biomedical literature. The structured databases for microbe–disease interactions are in limited amounts. In… read more here.
Keywords: microbe; learning model; microbe disease; disease interactions ... See more keywords