{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:09:22Z","timestamp":1740146962377,"version":"3.37.3"},"reference-count":31,"publisher":"Public Library of Science (PLoS)","issue":"5","license":[{"start":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T00:00:00Z","timestamp":1717027200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003977","name":"Israel Science Foundation","doi-asserted-by":"publisher","award":["1258\/21"],"award-info":[{"award-number":["1258\/21"]}],"id":[{"id":"10.13039\/501100003977","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001736","name":"German-Israeli Foundation for Scientific Research and Development","doi-asserted-by":"publisher","award":["I-1523-500.15\/2021"],"award-info":[{"award-number":["I-1523-500.15\/2021"]}],"id":[{"id":"10.13039\/501100001736","id-type":"DOI","asserted-by":"publisher"}]},{"name":"United States-Israel Binational Science Foundation (BSF), Jerusalem, Israel","award":["2020255"],"award-info":[{"award-number":["2020255"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>The human microbiome plays a crucial role in determining our well-being and can significantly influence human health. The individualized nature of the microbiome may reveal host-specific information about the health state of the subject. In particular, the microbiome is an ecosystem shaped by a tangled network of species-species and host-species interactions. Thus, analysis of the ecological balance of microbial communities can provide insights into these underlying interrelations. However, traditional methods for network analysis require many samples, while in practice only a single-time-point microbial sample is available in clinical screening. Recently, a method for the analysis of a single-time-point sample, which evaluates its \u2018network impact\u2019 with respect to a reference cohort, has been applied to analyze microbial samples from women with Gestational Diabetes Mellitus. Here, we introduce different variations of the network impact approach and systematically study their performance using simulated \u2018samples\u2019 fabricated via the Generalized Lotka-Volttera model of ecological dynamics. We show that the network impact of a single sample captures the effect of the interactions between the species, and thus can be applied to anomaly detection of shuffled samples, which are \u2018normal\u2019 in terms of species abundance but \u2018abnormal\u2019 in terms of species-species interrelations. In addition, we demonstrate the use of the network impact in binary and multiclass classifications, where the reference cohorts have similar abundance profiles but different species-species interactions. Individualized analysis of the human microbiome has the potential to improve diagnosis and personalized treatments.<\/jats:p>","DOI":"10.1371\/journal.pone.0301683","type":"journal-article","created":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T17:24:30Z","timestamp":1717089870000},"page":"e0301683","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Network impact of a single-time-point microbial 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