Author

Britney Orihuela

Lawrence Berkeley National Laboratory

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Microbiology· 2

Signature Assays, Organisms & Methods

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Organism:human· 2 Approach:machine learning (classical)· 2 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Research Focus:drug response / drug discovery· 1 Assay:proteomics· 1

Frequent Co-Authors

Adam M. DeutschbaueriD· 2 shared Adam P. ArkiniD· 2 shared Alexey Kazakov· 2 shared Avery J. C. NoonaniD· 2 shared Collis BouslimaniD· 2 shared Denish PiyaiD· 2 shared Flavien MaucourtiD· 2 shared Hemaa SelvakumariD· 2 shared

Activity by Year

2026-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersMicrobiology

Adam M. DeutschbauerORCID

Mutual co-authors in cluster

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2 papers

Recent·Most cited

A comprehensive phage-bacteria interaction atlas links phage lineage and capsule serotype to genome-guided machine learning prediction in Klebsiella pneumoniae

Hemaa Selvakumar, Avery J. C. Noonan, Ella R. Rotman, Mohamad Alayouni, Denish Piya, Flavien Maucourt, Sarshad Koderi Valappil, Madeline Svab, Britney Orihuela, Gina Cowser, Isabella Murray, Collis Bousliman, Alexey Kazakov, Adam M. Deutschbauer, Simon Roux, , Vivek K. Mutalik

This paper constructs a phage-bacteria interaction atlas for Klebsiella pneumoniae and develops a genome-guided machine learning model to predict phage-host interactions, outperforming traditional methods.

bioRxiv · Aug 17, 2026 · PreprintMicrobiology

Comprehensive interaction profiling and machine learning prediction of bacteriophage infectivity across clinically diverse Pseudomonas aeruginosa

Denish Piya, Avery J. C. Noonan, Hemaa Selvakumar, Mohamad Alayouni, Sarshad Koderi Valappil, Flavien Maucourt, Isabella Murray, Madeline Svab, Collis Bousliman, Matthew Heidenblut, Britney Orihuela, Alexey Kazakov, Hans K. Carlson, Yiyan Yao, Emerson Smith, , Vivek K. Mutalik

The paper develops a genome-based framework to predict bacteriophage infectivity against diverse Pseudomonas aeruginosa strains using machine learning and experimental data.

bioRxiv · May 20, 2026 · PreprintMicrobiology