{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:06:42Z","timestamp":1790813202677,"version":"4.1.0"},"reference-count":12,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"vor","delay-in-days":270,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Carnot Qualiment\u00ae","award":["#20 CARN 0026 01"],"award-info":[{"award-number":["#20 CARN 0026 01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Understanding interactions from microbiome data is a central aspect in microbial ecology, as it provides insights into ecosystem stability, disease mechanisms, and can be used to design synthetic communities. Current network inference tools reconstruct global networks from co-abundance data, which means they capture the overall correlation structure for the entire set of taxa considered. These approaches are computationally intensive and suboptimal when the focus is on the local neighborhood of one or a few taxa of interest.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We introduce NeighborFinder, a local network inference method that enables the targeted discovery of direct neighbors around a species of interest. Using cross-validated multiple linear regression with \u21131 penalty and microbiome-specific filters, our approach infers interpretable species-centered interactions, with F1 score \u2265 0.95 on simulated cohorts ranging from 250 to 1000 samples. This method is well-suited for large metagenomic datasets and is particularly valuable for exploratory studies where the targeted hypotheses outweigh the need for global community structure. The approach complements existing methods by being biologically intuitive and computationally efficient.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and Implementation<\/jats:title>\n                    <jats:p>The R package is available on CRAN https:\/\/CRAN.R-project.org\/package=NeighborFinder. The data and source code used to calculate performances and produce the use case example can be found respectively at: https:\/\/doi.org\/10.57745\/UPITJ0 and https:\/\/doi.org\/10.57745\/HJLWW4.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics Advances online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioadv\/vbag201","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T11:26:03Z","timestamp":1790594763000},"source":"Crossref","is-referenced-by-count":0,"title":["NeighborFinder: an R package inferring local microbial network around a species of 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