{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T21:35:59Z","timestamp":1778967359565,"version":"3.51.4"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1009343","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2021,9,20]],"date-time":"2021-09-20T00:00:00Z","timestamp":1632096000000}}],"reference-count":43,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T00:00:00Z","timestamp":1631059200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001348","name":"agency for science, technology and research","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001348","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012415","name":"Biomedical Research Council","doi-asserted-by":"publisher","award":["H18\/01\/a0\/016"],"award-info":[{"award-number":["H18\/01\/a0\/016"]}],"id":[{"id":"10.13039\/501100012415","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:title\/>\n                  <jats:p>The structure and function of diverse microbial communities is underpinned by ecological interactions that remain uncharacterized. With rapid adoption of next-generation sequencing for studying microbiomes, data-driven inference of microbial interactions based on abundance correlations is widely used, but with the drawback that ecological interpretations may not be possible. Leveraging cross-sectional microbiome datasets for unravelling ecological structure in a scalable manner thus remains an open problem. We present an expectation-maximization algorithm (BEEM-Static) that can be applied to cross-sectional datasets to infer interaction networks based on an ecological model (generalized Lotka-Volterra). The method exhibits robustness to violations in model assumptions by using statistical filters to identify and remove corresponding samples. Benchmarking against 10 state-of-the-art correlation based methods showed that BEEM-Static can infer presence and directionality of ecological interactions even with relative abundance data (AUC-ROC&gt;0.85), a task that other methods struggle with (AUC-ROC&lt;0.63). In addition, BEEM-Static can tolerate a high fraction of samples (up to 40%) being not at steady state or coming from an alternate model. Applying BEEM-Static to a large public dataset of human gut microbiomes (n = 4,617) identified multiple stable equilibria that better reflect ecological enterotypes with distinct carrying capacities and interactions for key species.<\/jats:p>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>BEEM-Static provides new opportunities for mining ecologically interpretable interactions and systems insights from the growing corpus of microbiome data.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pcbi.1009343","type":"journal-article","created":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T13:31:38Z","timestamp":1631107898000},"page":"e1009343","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":14,"title":["BEEM-Static: Accurate inference of ecological interactions from cross-sectional microbiome 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