About Sarah Alghamdi Sarah Alghamdi Ph.D. Student (former), Computer Science artificial intelligence statistical methods genomics healthcare Sarah Alghamdi is a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Research Interests Sarah's research interests include applications of artificial intelligence and using statistical methods to genomics and healthcare. Professional Profile Program Committee for the International Conference of Biomedical Ontology (ICBO 2022) Program Committee for the International Society of Molecular Biology (ISMB 2022) - Bio-Ontology track 2018-2021: Teaching Assistant positions: CS Events Presented Events Jul 16 - Jul 22, 2023 Ontology design patterns and methods for integrating phenotype ontologies Sarah Alghamdi, Ph.D. Student (former), Computer Science Jul 20, 09:00 - 10:00 B3 L5 R5209. bio-ontology phenotype ontologies Ontologies are widely used in various domains, including biomedical research, to structure information, represent knowledge, and analyze data. Combining ontologies from different domains is crucial for systematic data analysis and comparison of similar domains. This requires ontology composition, integration, and alignment, which involve creating new classes by reusing classes from different domains, aggregating types of ontologies within the same domain, and finding correspondences between ontologies within the same or similar domain. Apr 8 - Apr 14, 2018 Ontology Design Patterns for Combining Pathology and Anatomy: Application to Study Ageing and Longevity in Inbred Mouse Strains Sarah Alghamdi, Ph.D. Student (former), Computer Science Apr 10, 13:00 - 14:30 B9 R3120 biomedicine Ontologies data analysis semantic analysis computation techniques Abstract In biomedical research, ontologies are widely used to represent knowledge as well as annotate datasets. Many of the existing ontologies cover a single type of phenomena, such as a process, cell type, gene, pathological entity or anatomical structure. Consequently, it is required to use multiple ontologies to fully characterize the observations in the datasets. Although this allows precise annotation of different aspects of a given dataset, it limits our ability to use the ontologies in data analysis, as the ontologies are usually disconnected and their combination cannot be exploited
Ontology design patterns and methods for integrating phenotype ontologies Sarah Alghamdi, Ph.D. Student (former), Computer Science Jul 20, 09:00 - 10:00 B3 L5 R5209. bio-ontology phenotype ontologies Ontologies are widely used in various domains, including biomedical research, to structure information, represent knowledge, and analyze data. Combining ontologies from different domains is crucial for systematic data analysis and comparison of similar domains. This requires ontology composition, integration, and alignment, which involve creating new classes by reusing classes from different domains, aggregating types of ontologies within the same domain, and finding correspondences between ontologies within the same or similar domain.
Ontology Design Patterns for Combining Pathology and Anatomy: Application to Study Ageing and Longevity in Inbred Mouse Strains Sarah Alghamdi, Ph.D. Student (former), Computer Science Apr 10, 13:00 - 14:30 B9 R3120 biomedicine Ontologies data analysis semantic analysis computation techniques Abstract In biomedical research, ontologies are widely used to represent knowledge as well as annotate datasets. Many of the existing ontologies cover a single type of phenomena, such as a process, cell type, gene, pathological entity or anatomical structure. Consequently, it is required to use multiple ontologies to fully characterize the observations in the datasets. Although this allows precise annotation of different aspects of a given dataset, it limits our ability to use the ontologies in data analysis, as the ontologies are usually disconnected and their combination cannot be exploited
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