{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T06:15:17Z","timestamp":1775542517316,"version":"3.50.1"},"reference-count":23,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T00:00:00Z","timestamp":1775520000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Microbiol."],"abstract":"<jats:p>\n                    Deciphering microbial symbiosis in robust environmental and host-associated systems increasingly depends on the reliable recovery of weak biological signals from sequencing data dominated by non-target background. In practice, low-abundance symbiont-derived signatures are often obscured by background DNA, recurrent technical artifacts, and context-dependent false-positive calls, limiting the accuracy of downstream ecological and functional inference. Here, we present SymbioFilter, a hybrid expert system designed to improve the specificity and stability of rare-signal detection in host-associated microbiome sequencing data with substantial background noise. (i.e., overwhelming host-derived DNA, recurrent alignment errors, and sequencing artifacts). Specifically, this framework is intended for specialized usage cases where standard host-depletion strategies fail, such as capturing minor microbe-induced host somatic mutations or rare microbial homologs, which reflect subtle and intimate interactions between microbes and the host. SymbioFilter integrates three coordinated layers: (i)ensemble-based candidate detection, (ii)machine-learned background\/noise discrimination using an XGBoost classifier, and (iii) rule-guided suppression of artifact-prone genomic regions using curated repetitive and blacklist annotations. Rather than relying on a single decision rule, the framework combines probabilistic classification with expert-defined constraints to preserve weak true signals while reducing recurrent false positives. This design specifically targets analytical failure modes common to host-dominated and low-input datasets, where precision is frequently compromised by rare-event noise. As a stringent proof-of-concept benchmark, we evaluated SymbioFilter in synthetic gradient spike-in datasets. Crucially, to ensure rigorous computational evaluation with an absolute ground truth\u2014a standard that is currently unattainable in complex, real-world microbiome samples at ultra-low abundances\u2014we utilized plasma cfDNA-like low-frequency conditions as a highly controlled, demanding proxy. Across gradient abundance levels, the framework consistently reduced false-positive inflation, improved agreement with the known ground truth, and maintained stable classification performance. Compared with a baseline pipeline and a widely used variant-calling workflow, SymbioFilter achieved lower mean squared error, stronger true-positive\/true-negative balance, and consistently high precision-recall behavior, particularly under the most challenging low-abundance settings. Although validated here in a proxy benchmark environment, the computational principles of SymbioFilter address a broader class of sequencing problems central to microbial symbiosis research: identifying rare, functionally relevant biological signals in complex, noise-prone backgrounds. By providing a transferable, modular, and open computational strategy for robust signal recovery, SymbioFilter offers a useful methodological foundation for future studies of host-microbe interactions, resilient community assembly, and symbiosis-associated ecosystem stability. The code is freely available for academic use at\n                    <jats:ext-link>https:\/\/github.com\/hello-json\/SymbioFilter<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.3389\/fmicb.2026.1821073","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T05:34:46Z","timestamp":1775540086000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Hybrid expert system for robust detection of rare sequence signals: a computational proof-of-concept in host-dominated backgrounds"],"prefix":"10.3389","volume":"17","author":[{"given":"Shenjie","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an Jiaotong University","place":["Xi\u2019an, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an Jiaotong University","place":["Xi\u2019an, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyu","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an Jiaotong University","place":["Xi\u2019an, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinkai","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an Jiaotong University","place":["Xi\u2019an, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruidan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Xi\u2019an People\u2019s Hospital (Xi\u2019an Fourth Hospital)","place":["Xi\u2019an, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,4,7]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"e65088","DOI":"10.7554\/eLife.65088","article-title":"Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3.","volume":"10","author":"Beghini","year":"2021","journal-title":"eLife"},{"key":"B2","doi-asserted-by":"publisher","first-page":"852","DOI":"10.1038\/s41587-019-0209-9","article-title":"Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2.","volume":"37","author":"Bolyen","year":"2019","journal-title":"Nat. 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