{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T00:59:02Z","timestamp":1775264342899,"version":"3.50.1"},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T00:00:00Z","timestamp":1702339200000},"content-version":"vor","delay-in-days":20,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The stability of the gut microenvironment is inextricably linked to human health, with the onset of many diseases accompanied by dysbiosis of the gut microbiota. It has been reported that there are differences in the microbial community composition between patients and healthy individuals, and many microbes are considered potential biomarkers. Accurately identifying these biomarkers can lead to more precise and reliable clinical decision-making. To improve the accuracy of microbial biomarker identification, this study introduces WSGMB, a computational framework that uses the relative abundance of microbial taxa and health status as inputs. This method has two main contributions: (1) viewing the microbial co-occurrence network as a weighted signed graph and applying graph convolutional neural network techniques for graph classification; (2) designing a new architecture to compute the role transitions of each microbial taxon between health and disease networks, thereby identifying disease-related microbial biomarkers. The weighted signed graph neural network enhances the quality of graph embeddings; quantifying the importance of microbes in different co-occurrence networks better identifies those microbes critical to health. Microbes are ranked according to their importance change scores, and when this score exceeds a set threshold, the microbe is considered a biomarker. This framework\u2019s identification performance is validated by comparing the biomarkers identified by WSGMB with actual microbial biomarkers associated with specific diseases from public literature databases. The study tests the proposed computational framework using actual microbial community data from colorectal cancer and Crohn\u2019s disease samples. It compares it with the most advanced microbial biomarker identification methods. The results show that the WSGMB method outperforms similar approaches in the accuracy of microbial biomarker identification.<\/jats:p>","DOI":"10.1093\/bib\/bbad448","type":"journal-article","created":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T17:47:05Z","timestamp":1702403225000},"source":"Crossref","is-referenced-by-count":8,"title":["WSGMB: weight signed graph neural network for microbial biomarker identification"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5481-1285","authenticated-orcid":false,"given":"Shuheng","family":"Pan","sequence":"first","affiliation":[{"name":"Institute of Data and Information, Tsinghua Shenzhen International Graduate School, Tsinghua University , Shenzhen 518005 , China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0751-9823","authenticated-orcid":false,"given":"Xinyi","family":"Jiang","sequence":"additional","affiliation":[{"name":"Institute of Data and Information, Tsinghua Shenzhen International Graduate School, Tsinghua University , Shenzhen 518005 , China"}]},{"given":"Kai","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Data and Information, Tsinghua Shenzhen International Graduate School, Tsinghua University , Shenzhen 518005 , China"}]}],"member":"286","published-online":{"date-parts":[[2023,12,11]]},"reference":[{"issue":"1","key":"2023121211060305100_ref1","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1021\/acs.chemrev.2c00431","article-title":"Microbiome and human health: current understanding, engineering, and enabling technologies","volume":"123","author":"Aggarwal","year":"2022","journal-title":"Chem Rev"},{"issue":"2","key":"2023121211060305100_ref2","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1093\/cei\/uxac057","article-title":"Gut microbiome and autoimmune disorders","volume":"209","author":"Shaheen","year":"2022","journal-title":"Clin Exp Immunol"},{"issue":"1","key":"2023121211060305100_ref3","doi-asserted-by":"crossref","first-page":"159","DOI":"10.2337\/dc14-0769","article-title":"Insights into the role of the microbiome in obesity and type 2 diabetes","volume":"38","author":"Hartstra","year":"2015","journal-title":"Diabetes Care"},{"issue":"02","key":"2023121211060305100_ref4","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1055\/s-0042-1760679","article-title":"Role of bacteria in the development of colorectal cancer","volume":"36","author":"Thomas","year":"2023","journal-title":"Clin Colon Rectal Surg"},{"issue":"3","key":"2023121211060305100_ref5","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1586\/14737140.2015.992785","article-title":"Cancer and the microbiome: potential applications as new tumor biomarker","volume":"15","author":"Shahanavaj","year":"2015","journal-title":"Expert Rev Anticancer Ther"},{"key":"2023121211060305100_ref6","doi-asserted-by":"crossref","first-page":"2300043","DOI":"10.1002\/mnfr.202300043","article-title":"The gut microbiome and autoimmune hepatitis: implications for early diagnostic biomarkers and novel therapies","author":"Li","year":"2023","journal-title":"Mol Nutr Food Res"},{"issue":"1","key":"2023121211060305100_ref7","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1093\/bib\/bbx104","article-title":"A broken promise: microbiome differential abundance methods do not control the false discovery rate","volume":"20","author":"Hawinkel","year":"2019","journal-title":"Brief Bioinform"},{"issue":"1","key":"2023121211060305100_ref8","doi-asserted-by":"crossref","first-page":"56","DOI":"10.3390\/microorganisms11010056","article-title":"Predicting the postmortem interval based on gravesoil microbiome data and a random forest model","volume":"11","author":"Cui","year":"2022","journal-title":"Microorganisms"},{"issue":"02","key":"2023121211060305100_ref9","doi-asserted-by":"crossref","first-page":"091","DOI":"10.1055\/s-0043-1760863","article-title":"What is the microbiome? 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