Author

Sarah M. Alghamdi

King Abdullah University of Science and TechnologyORCID 0000-0001-5544-7166Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Organism:mouse· 2 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Research Focus:gene regulatory network inference· 1 Organism:multiple species· 1

Frequent Co-Authors

Paul N. SchofieldiD· 2 shared Robert HoehndorfiD· 2 shared Alayne CuzickiD· 1 shared Anita R. CaroniD· 1 shared Anna V. AnagnostopoulosiD· 1 shared Arwa IbrahimiD· 1 shared Beth A. SundbergiD· 1 shared Chris MungalliD· 1 shared

Activity by Year

2019-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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2 shared papersBioinformatics

Paul N. SchofieldORCID

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3 papers

Recent·Most cited

The Unified Phenotype Ontology (uPheno): A framework for cross-species integrative phenomics

Nicolas Matentzoglu, Susan M. Bello, Ray Stefancsik, Sarah M. Alghamdi, Anna V. Anagnostopoulos, James P. Balhoff, Meghan A. Balk, Yvonne M. Bradford, Yasemin Bridges, Tiffany J. Callahan, Harry Caufield, Alayne Cuzick, Leigh Carmody, Anita R. Caron, Vinícius de Souza, , David Osumi-Sutherland

The paper introduces uPheno, a framework for cross-species phenomics integration by harmonizing phenotype data across species and ontologies.

Genetics · Mar 1, 2025Peer-reviewedBioinformatics

How much do model organism phenotypes contribute to the computational identification of human disease genes?

Sarah M. Alghamdi, Paul N. Schofield, Robert Hoehndorf

The paper assesses how model organism phenotypes contribute to identifying human disease genes and finds that only mouse phenotypes accurately predict human gene-disease associations.

Disease Models & Mechanisms · Jun 27, 2022Peer-reviewedBioinformatics

Quantitative evaluation of ontology design patterns for combining pathology and anatomy ontologies

Sarah M. Alghamdi, Beth A. Sundberg, John P. Sundberg, Paul N. Schofield, Robert Hoehndorf

The paper evaluates ontology design patterns for combining pathology and anatomy ontologies using an aging mice dataset, showing how different patterns perform in analysis tasks.

Scientific Reports · Mar 11, 2019Peer-reviewedBioinformatics