Background: Recent research has provided fascinating indications and evidence that the host health is linked to its microbial inhabitants. Due to the development of high-throughput sequencing technologies, more and more data covering microbial composition changes in different disease types are emerging. However, this information is dispersed over a wide variety of medical and biomedical disciplines.
Description: Disbiome is a database which collects and presents published microbiota-disease information in a standardized way. The diseases are classified using the MedDRA classification system and the micro-organisms are linked to their NCBI and SILVA taxonomy. Finally, each study included in the Disbiome database is assessed for its reporting quality using a standardized questionnaire.
Conclusions: Disbiome is the first database giving a clear, concise and up-to-date overview of microbial composition differences in diseases, together with the relevant information of the studies published. The strength of this database lies within the combination of the presence of references to other databases, which enables both specific and diverse search strategies within the Disbiome database, and the human annotation which ensures a simple and structured presentation of the available data.
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http://dx.doi.org/10.1186/s12866-018-1197-5 | DOI Listing |
Brief Bioinform
September 2024
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Qingshuihe Campus, 2006 Xiyuan Avenue, West District, High-tech Zone, Chengdu, Sichuan 610054, China.
Background: Microorganisms inhabit various regions of the human body and significantly contribute to numerous diseases. Predicting the associations between microbes and diseases is crucial for understanding pathogenic mechanisms and informing prevention and treatment strategies. Biological experiments to determine these associations are time-consuming and costly.
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July 2024
Big Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha, China.
Introduction: Accumulating evidence shows that human health and disease are closely related to the microbes in the human body.
Methods: In this manuscript, a new computational model based on graph attention networks and sparse autoencoders, called GCANCAE, was proposed for inferring possible microbe-disease associations. In GCANCAE, we first constructed a heterogeneous network by combining known microbe-disease relationships, disease similarity, and microbial similarity.
Front Microbiol
September 2023
Faculty of Pediatrics, The Chinese PLA General Hospital, Beijing, China.
Background: Microbes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases.
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June 2023
The Second Department of Oncology, Beidahuang Industry Group General Hospital, Harbin, China.
Introduction: Identification of complex associations between diseases and microbes is important to understand the pathogenesis of diseases and design therapeutic strategies. Biomedical experiment-based Microbe-Disease Association (MDA) detection methods are expensive, time-consuming, and laborious.
Methods: Here, we developed a computational method called SAELGMDA for potential MDA prediction.
Front Microbiol
March 2023
College of Computer Science and Technology, Hengyang Normal University, Hengyang, China.
Researches have demonstrated that microorganisms are indispensable for the nutrition transportation, growth and development of human bodies, and disorder and imbalance of microbiota may lead to the occurrence of diseases. Therefore, it is crucial to study relationships between microbes and diseases. In this manuscript, we proposed a novel prediction model named MADGAN to infer potential microbe-disease associations by combining biological information of microbes and diseases with the generative adversarial networks.
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