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