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Machine learning identifies key microbial signatures for Salmonella enteritidis colonization and persistence in broiler ceca.

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Bibliographic Details
Title:	Machine learning identifies key microbial signatures for Salmonella enteritidis colonization and persistence in broiler ceca.
Authors:	Poudel S; Department of Poultry Science, Auburn University, United States. Electronic address: SZP0208@auburn.edu., Zelaya FAC; Department of Poultry Science, Auburn University, United States., Guzman EGG; Department of Poultry Science, Auburn University, United States., Rahman FA; Department of Poultry Science, Auburn University, United States., Naeem M; Department of Poultry Science, Auburn University, United States., Adhikari Y; Department of Poultry Science, Auburn University, United States., Manjankattil S; Department of Poultry Science, Auburn University, United States., Pacheco W; Department of Poultry Science, Auburn University, United States., Hauck R; Department of Poultry Science, Auburn University, United States; Department of Pathobiology, Auburn University, United States., Bourassa D; Department of Poultry Science, Auburn University, United States., Macklin K; Department of Poultry Science, Mississippi State University, United States.
Source:	Poultry science [Poult Sci] 2026 Sep 14; Vol. 105 (12), pp. 107753. Date of Electronic Publication: 2026 Sep 14.
Publication Type:	Journal Article
Journal Info:	Publisher: Elsevier Country of Publication: England NLM ID: 0401150 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1525-3171 (Electronic) Linking ISSN: 00325791 NLM ISO Abbreviation: Poult Sci Subsets: MEDLINE
Database:	MEDLINE Ultimate

Description
ISSN:	1525-3171
DOI:	10.1016/j.psj.2026.107753