Sarah M. Alghamdi

@computing.kau.edu.sa

Faculty of Computing and Information Technology
KAU: King Abdulaziz University

Sarah M. Alghamdi


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https://researchid.co/smalghamdi
Sarah M. Alghamdi is a faculty member in the Computer Science department at King Abdulaziz University (KAU) in Rabigh, Saudi Arabia. Sarah's research interest is in Artificial Intelligence applications in biomedical applications, specifically in leveraging biomedical ontologies to enable computational reasoning over complex biomedical data. Sarah received her B.S. degree in Computer Science and Artificial Intelligence track from KAU in 2014. She then received her M.S. in Computer Science from KAUST in 2018. Then she received her PhD in computer science from KAUST in 2023.

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Artificial Intelligence
84

Scholar Citations

5

Scholar h-index

3

Scholar i10-index

RECENT SCHOLAR PUBLICATIONS

  • The Unified Phenotype Ontology: a framework for cross-species integrative phenomics
    N Matentzoglu, SM Bello, R Stefancsik, SM Alghamdi, ...
    Genetics 229 (3), iyaf027 , 2025
    2025.0
    Citations: 16
  • Decoding fatty acid dynamics in planktonic communitis of the Red Sea: Nutritional perspective
    S Alghamdi
    2023.0
  • Improving the classification of cardinality phenotypes using collections
    SM Alghamdi, R Hoehndorf
    Journal of biomedical semantics 14 (1), 9 , 2023
    2023.0
    Citations: 2
  • Ontology design patterns and methods for integrating phenotype ontologies
    SM Alghamdi
    2023.0
    Citations: 1
  • First person-Sarah Alghamdi
    S Alghamdi
    Disease Models & Mechanisms 15 (7) , 2022
    2022.0
  • Contribution of model organism phenotypes to the computational identification of human disease genes
    SM Alghamdi, PN Schofield, R Hoehndorf
    Disease models & mechanisms 15 (7), dmm049441 , 2022
    2022.0
    Citations: 21
  • Machine Learning with Biomedical Ontologies
    S Alghamdi, R Hoehndorf, M Kulmanov, S Toonsi, F Zhapa-Camacho
    International SWAT4HCLS Conference , 2022
    2022.0
  • A-LIOn-Alignment Learning through Inconsistency negatives of the aligned Ontologies
    SM Alghamdi, F Zhapa-Camacho, R Hoehndorf
    CEUR-WS , 2022
    2022.0
    Citations: 8
  • Ontological Analysis of Collection Improves Classification of Cardinality Phenotypes.
    SM Alghamdi, R Hoehndorf
    ICBO, 1-2 , 2022
    2022.0
  • How much do model organism phenotypes contribute to the computational identification of human disease genes?
    SM Alghamdi, PN Schofield, R Hoehndorf
    bioRxiv, 2021.12. 24.474099 , 2021
    2021.0
  • bio-ontology-research-group/mo-phenotype-analysis: Model organism phenotypes contribution in predicting gene disease associations
    SM Alghamdi, PN Schofield, R Hoehndorf
    Github , 2021
    2021.0
    Citations: 2
  • bio-ontology-research-group/mowl: mOWL: Machine Learning library with Ontologies
    F Zhapa-Camacho, M Kulmanov, R Hoehndorf, SM Alghamdi, C Jahn, ...
    Github , 2020
    2020.0
  • Hyaline arteriolosclerosis in 30 strains of aged inbred mice
    TK Cooper, KA Silva, VE Kennedy, S Alghamdi, R Hoehndorf, ...
    Veterinary pathology 56 (5), 799-806 , 2019
    2019.0
    Citations: 4
  • Nail abnormalities identified in an ageing study of 30 inbred mouse strains
    SC Linn, AM Mustonen, KA Silva, VE Kennedy, BA Sundberg, ...
    Experimental dermatology 28 (4), 383-390 , 2019
    2019.0
    Citations: 8
  • Quantitative evaluation of ontology design patterns for combining pathology and anatomy ontologies
    SM Alghamdi, BA Sundberg, JP Sundberg, PN Schofield, R Hoehndorf
    Scientific reports 9 (1), 4025 , 2019
    2019.0
    Citations: 21
  • Ontology Design Patterns for Combining Pathology and Anatomy: Application to Study Aging and Longevity in Inbred Mouse Strains
    SM Alghamdi
    2018.0
  • 665 Nail lesions in 30 old inbred mouse strains
    SC Linn, AM Mustonen, KA Silva, VE Kennedy, BA Sundberg, ...
    Journal of Investigative Dermatology 138 (5), S113 , 2018
    2018.0
  • bio-ontology-research-group/mpath-ma: Code used to evaluate combinations of MPATH and MA
    BA Sundberg, JP Sundberg, PN Schofield, R Hoehndorf, SM Alghamdi
    Github , 2018
    2018.0
    Citations: 1
  • RESEARCH ARTICLE Contribution of model organism phenotypes to the computational identification of human disease genes
    SM Alghamdi, PN Schofield, R Hoehndorf

MOST CITED SCHOLAR PUBLICATIONS

  • Contribution of model organism phenotypes to the computational identification of human disease genes
    SM Alghamdi, PN Schofield, R Hoehndorf
    Disease models & mechanisms 15 (7), dmm049441 , 2022
    2022.0
    Citations: 21
  • Quantitative evaluation of ontology design patterns for combining pathology and anatomy ontologies
    SM Alghamdi, BA Sundberg, JP Sundberg, PN Schofield, R Hoehndorf
    Scientific reports 9 (1), 4025 , 2019
    2019.0
    Citations: 21
  • The Unified Phenotype Ontology: a framework for cross-species integrative phenomics
    N Matentzoglu, SM Bello, R Stefancsik, SM Alghamdi, ...
    Genetics 229 (3), iyaf027 , 2025
    2025.0
    Citations: 16
  • A-LIOn-Alignment Learning through Inconsistency negatives of the aligned Ontologies
    SM Alghamdi, F Zhapa-Camacho, R Hoehndorf
    CEUR-WS , 2022
    2022.0
    Citations: 8
  • Nail abnormalities identified in an ageing study of 30 inbred mouse strains
    SC Linn, AM Mustonen, KA Silva, VE Kennedy, BA Sundberg, ...
    Experimental dermatology 28 (4), 383-390 , 2019
    2019.0
    Citations: 8
  • Hyaline arteriolosclerosis in 30 strains of aged inbred mice
    TK Cooper, KA Silva, VE Kennedy, S Alghamdi, R Hoehndorf, ...
    Veterinary pathology 56 (5), 799-806 , 2019
    2019.0
    Citations: 4
  • Improving the classification of cardinality phenotypes using collections
    SM Alghamdi, R Hoehndorf
    Journal of biomedical semantics 14 (1), 9 , 2023
    2023.0
    Citations: 2
  • bio-ontology-research-group/mo-phenotype-analysis: Model organism phenotypes contribution in predicting gene disease associations
    SM Alghamdi, PN Schofield, R Hoehndorf
    Github , 2021
    2021.0
    Citations: 2
  • Ontology design patterns and methods for integrating phenotype ontologies
    SM Alghamdi
    2023.0
    Citations: 1
  • bio-ontology-research-group/mpath-ma: Code used to evaluate combinations of MPATH and MA
    BA Sundberg, JP Sundberg, PN Schofield, R Hoehndorf, SM Alghamdi
    Github , 2018
    2018.0
    Citations: 1
  • Decoding fatty acid dynamics in planktonic communitis of the Red Sea: Nutritional perspective
    S Alghamdi
    2023.0
  • First person-Sarah Alghamdi
    S Alghamdi
    Disease Models & Mechanisms 15 (7) , 2022
    2022.0
  • Machine Learning with Biomedical Ontologies
    S Alghamdi, R Hoehndorf, M Kulmanov, S Toonsi, F Zhapa-Camacho
    International SWAT4HCLS Conference , 2022
    2022.0
  • Ontological Analysis of Collection Improves Classification of Cardinality Phenotypes.
    SM Alghamdi, R Hoehndorf
    ICBO, 1-2 , 2022
    2022.0
  • How much do model organism phenotypes contribute to the computational identification of human disease genes?
    SM Alghamdi, PN Schofield, R Hoehndorf
    bioRxiv, 2021.12. 24.474099 , 2021
    2021.0
  • bio-ontology-research-group/mowl: mOWL: Machine Learning library with Ontologies
    F Zhapa-Camacho, M Kulmanov, R Hoehndorf, SM Alghamdi, C Jahn, ...
    Github , 2020
    2020.0
  • Ontology Design Patterns for Combining Pathology and Anatomy: Application to Study Aging and Longevity in Inbred Mouse Strains
    SM Alghamdi
    2018.0
  • 665 Nail lesions in 30 old inbred mouse strains
    SC Linn, AM Mustonen, KA Silva, VE Kennedy, BA Sundberg, ...
    Journal of Investigative Dermatology 138 (5), S113 , 2018
    2018.0
  • RESEARCH ARTICLE Contribution of model organism phenotypes to the computational identification of human disease genes
    SM Alghamdi, PN Schofield, R Hoehndorf