Ontologies

AI4GH Seminar Series| Artificial Intelligence for Genomics and Health: Introduction and Overview

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B2 R5220

Abstract Genomics and health are prime areas for the development and application of novel intelligent algorithms due to a large amount of available research data, the high complexity of the problem, and the significant social and economic benefits of improving health outcomes in humans. In the past, genomics and health have been one of the key drivers in the development of Artificial Intelligence methods, including machine learning, expert systems, or graph-based algorithms. I will introduce the new seminar series on Artificial Intelligence for Genomics and Health (AI4GH), and then discuss our

Ontology Design Patterns for Combining Pathology and Anatomy: Application to Study Ageing and Longevity in Inbred Mouse Strains

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B9 R3120

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

AI4GH Seminar Series - Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations

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B2 R5220

Sequencing has identified millions of somatic mutations in human cancers. Identifying and distinguishing cancer driver genes amongst the millions of candidate mutations remains a major challenge.

Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations

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B3 R5209

Abstract Sequencing has identified millions of somatic mutations in human cancers. Identifying and distinguishing cancer driver genes amongst the millions of candidate mutations remains a major challenge. Accurate identification of driver genes and driver mutations is critical for advancing cancer research and personalizing treatment based on accurate stratification of patients. Due to inter-tumor genetic heterogeneity, many driver mutations within a gene occur at low frequencies, which make it challenging to distinguish them from other non-driver mutations. Motivated by these challenges, we

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

1 min read ·
PhD Student Sarah M Alghamdi from the Bio-Ontology Research Group (BORG) explains how phenotype ontologies help in identifying disease genes through phenotype comparison. Her research was picked as a highlight in the July 2022 issue of Disease Models and Mechanisms Journal.

PhD Dissertation Defense | Ontology design patterns and methods for integrating phenotype ontologies

1 min read ·
CBRC PhD student Sarah Alghamdi successfully defended her dissertation last week. She has been an essential member of the Bio-Ontology Research Group (BORG) since 2015.Her research interests include the applications of artificial intelligence and statistical methods to genomics and healthcare.

Sara Althubaiti

Ph.D. Student, Computer Science
Sara Althubaiti is currently 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. Prior to this, Sara received her master's degree in computer science with a focus on bioinformatics from KAUST in December 2018. Research Interests Sara's research interests include bioinformatics, text mining, ontologies, and cancer. Her research focuses on applying machine learning methods in cancer biology and development specifically in the field of finding driver genes and mutations in

Salwa Al Ahmari

Visiting Student, Bio-Ontology Research Group
Salwa Al Ahmari was a visiting student ​at Professor Robert Hoehndorf's Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST). During her six months (Jan 2016 – Jun 2016) internship at KAUST, she worked on text mining techniques for Ontologies in CBRC.

Patrick Westphal

Visiting Student, Bio-Ontology Research Group
Patrick Westphal was a visiting student at Professor Robert Hoehndorf's Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST). During his three months (Jan 2016 – Mar 2016) internship at the Computational Bioscience Research Center (CBRC), he worked with the Bio-Ontology Research Group where he contributed to different projects aiming at finding correlations and causalities in relations between genotypes and phenotypes, and genes, diseases, and drugs. The applied techniques were usually backed by OWL ontologies or knowledge bases expressed in other