An integrated platform for Brucella with knowledge graph technology: From genomic analysis to epidemiological projection

Author:

Ma Fubo,Xiao Ming,Zhu Lin,Jiang Wen,Jiang Jizhe,Zhang Peng-Fei,Li Kang,Yue Min,Zhang Le

Abstract

Motivation:Brucella, the causative agent of brucellosis, is a global zoonotic pathogen that threatens both veterinary and human health. The main sources of brucellosis are farm animals. Importantly, the bacteria can be used for biological warfare purposes, requiring source tracking and routine surveillance in an integrated manner. Additionally, brucellosis is classified among group B infectious diseases in China and has been reported in 31 Chinese provinces to varying degrees in urban areas. From a national biosecurity perspective, research on brucellosis surveillance has garnered considerable attention and requires an integrated platform to provide researchers with easy access to genomic analysis and provide policymakers with an improved understanding of both reported patients and detected cases for the purpose of precision public health interventions.Results: For the first time in China, we have developed a comprehensive information platform for Brucella based on dynamic visualization of the incidence (reported patients) and prevalence (detected cases) of brucellosis in mainland China. Especially, our study establishes a knowledge graph for the literature sources of Brucella data so that it can be expanded, queried, and analyzed. When similar “epidemiological comprehensive platforms” are established in the distant future, we can use knowledge graph to share its information. Additionally, we propose a software package for genomic sequence analysis. This platform provides a specialized, dynamic, and visual point-and-click interface for studying brucellosis in mainland China and improving the exploration of Brucella in the fields of bioinformatics and disease prevention for both human and veterinary medicine.

Funder

National Key Research and Development Program of China

China Postdoctoral Science Foundation

Fundamental Research Funds for the Central Universities

Publisher

Frontiers Media SA

Subject

Genetics (clinical),Genetics,Molecular Medicine

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Using Knowledge graph and Quantum Computing to Optimize the Comprehensive Mental Health Adaptive Test System;2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);2023-12-05

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