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Predicting microbe-disease association Based on Heterogeneous Network and Global Graph Feature Learning

Chinese Journal of Electronics 2022年第2期31卷 345-353页

作者： WANG Yueyue LEI Xiujuan PAN Yi School of Computer Science Shaanxi Normal University Department of Computer Science Georgia State University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences

Numerous microbes inhabit human body,making a vast difference in human health. Hence, discovering associations between microbes and diseases is beneficial to disease prevention and treatment. In this study,we develop a prediction method by learning global graph feature on the heterogeneous network(called HNGFL).Firstly, a heterogeneous network is integrated by known microbe-disease associations and multiple *** on microbe Gaussian interaction profile(GIP) kernel similarity, we consider different effects of these microbes on organs in the human body to further improve microbe similarity. For disease similarity network, we combine GIP kernel similarity, disease semantic similarity and disease-symptom similarity. And then, an embedding algorithm called GraRep is used to learn global structural information for this network. According to vector feature of every node, we utilize support vector machine classifier to calculate the score for each microbe-disease pair. HNGFL achieves a reliable performance in cross validation, outperforming the compared methods. In addition, we carry out case studies of three diseases. Results show that HNGFL can be considered as a reliable method for microbe-disease association prediction.

关键词： microbe-disease association Heterogeneous network Global graph feature learning GraRep

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Quorum sensing-based interactions among drugs,microbes,and diseases

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Science China(Life Sciences) 2023年第1期66卷 137-151页

作者： Shengbo Wu Shujuan Yang Manman Wang Nan Song Jie Feng Hao Wu Aidong Yang Chunjiang Liu Yanni Li Fei Guo Jianjun Qiao School of Chemical Engineering and Technology Tianjin UniversityTianjin 300072China School of Computer Science and Engineering Central South UniversityChangsha 410083China State Key Laboratory of Chemical Engineering Tianjin UniversityTianjin 300072China Collaborative Innovation Center of Chemical Science and Engineering(Tianjin) Tianjin 300072China Key Laboratory of Systems Bioengineering Ministry of Education(Tianjin University)Tianjin 300072China School of Computer Science and Technology College of Intelligence and ComputingTianjin UniversityTianjin 300350China Department of Engineering Science University of OxfordOxford OX13PJUK Institute of Shaoxing Tianjin UniversityShaoxing 312300China

Many diseases and health conditions are closely related to various microbes,which participate in complex interactions with diverse drugs;nonetheless,the detailed targets of such drugs remain to be *** existing studies have reported causal associations among drugs,gut microbes,or diseases,calling for a workflow to reveal their intricate *** this study,we developed a systematic workflow comprising three modules to construct a Quorum Sensing-based Drug-microbe-disease(QSDMD)database(http://***/),which includes diverse interactions for more than 8,000 drugs,163 microbes,and 42 common *** interactions between microbes and more than 8,000 drugs have been systematically studied by targeting microbial QS receptors combined with a docking-based virtual screening technique and in vitro experimental ***,we have constructed a QS-based drug-receptor interaction network,proposed a systematic framework including various drug-receptor-microbe-disease connections,and mapped a paradigmatic circular interaction network based on the QS-DMD,which can provide the underlying QS-based mechanisms for the reported causal *** QS-DMD will promote an understanding of personalized medicine and the development of potential therapies for diverse *** work contributes to a paradigm for the construction of a molecule-receptor-microbe-disease interaction network for human health that may form one of the key knowledge maps of precision medicine in the future.

关键词： quorum sensing drugs microbe-disease association microbe-host interaction gut microbiota personalized medicine

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MicroPhenoDB Associates Metagenomic Data with Pathogenic microbes, Microbial Core Genes, and Human disease Phenotypes

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Genomics, Proteomics & Bioinformatics 2020年第6期18卷 760-772页

作者： Guocai Yao Wenliang Zhang Minglei Yang Huan Yang Jianbo Wang Haiyue Zhang Lai Wei Zhi Xie Weizhong Li Zhongshan School of Medicine Sun Yat-sen UniversityGuangzhou 510080China Center for Precision Medicine Sun Yat-sen UniversityGuangzhou 510080China State Key Lab of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangzhou 500060China Key Laboratory of Tropical disease Control of Ministry of Education Sun Yat-Sen UniversityGuangzhou 510080China

microbes play important roles in human health and *** interaction between microbes and hosts is a reciprocal relationship,which remains largely *** computational resources lack manually and consistently curated data to connect metagenomic data to pathogenic microbes,microbial core genes,and disease *** developed the MicroPhenoDB database by manually curating and consistently integrating microbe-disease association *** provides 5677 non-redundant associations between 1781 microbes and 542 human disease phenotypes across more than 22 human body *** also provides 696,934 relationships between 27,277 unique clade-specific core genes and 685 *** phenotypes are classified and described using the Experimental Factor Ontology(EFO).A refined score model was developed to prioritize the associations based on evidential *** sequence search option in MicroPhenoDB enables rapid identification of existing pathogenic microbes in samples without running the usual metagenomic data processing and *** offers data browsing,searching,and visualization through user-friendly web interfaces and web service application programming *** is the first database platform to detail the relationships between pathogenic microbes,core genes,and disease *** will accelerate metagenomic data analysis and assist studies in decoding microbes related to human *** is available through http://***/microphenodb and http://***/microphenodb.

关键词： Pathogenic microbes Metagenomic data disease phenotypes microbe-disease association COVID-19

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