data analysis

David De Jesus

Postdoctoral Research Fellow, Applied Mathematics and Computational Science
David's research focuses on the regularity of solutions to degenerate elliptic and parabolic partial differential equations as well as free boundary problems.

Amal Alghamdi

Nizar Ajeeb

Full Time Researcher Full at King Abdullah University of Science and Technology.

Bio-ontologies and their role in analyzing personal genome data

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KAUST

The aim of the tutorial is to introduce recent advances in using biomedical ontologies for data analysis, in particular for the interpretation of personal genome data and identifying causal variants underlying a phenotype or disease.

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 - Vector Representation of Biological Entities Based on Ontologies and Their Annotations

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B2 R5220

Biological knowledge is widely represented in the form of ontology-based annotations: ontologies describe the phenomena assumed to exist within a domain, and the annotations associate a biological entity with a set of phenomena within the domain.

Neuro-symbolic systems in the life sciences

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B9 H1 R2322

Abstract The life sciences have invested significant resources in the development and application of semantic technologies to make research data accessible and interlinked, and to enable the integration and analysis of data. Utilizing the semantics associated with research data in data analysis approaches is often challenging. Now, novel methods are becoming available that combine symbolic methods and statistical methods in Artificial Intelligence. In my talk, I will describe how to generate knowledge graph embeddings for analysis of biological and biomedical data. Brief Biography Robert

Yang Liu

Ph.D. Student, Computer Science
Yang Liu is an M.S./Ph.D. candidate in the KAUST Bio-Ontology research group under the supervision of Professor Robert Hoehndorf. Before joining KAUST. Research Interests Yang's interests include bioinformatics, machine learning and human genomics. She is interested in building models for human disease diagnosis and prognosis, especially for cancer, and applying statistical methods to analyze human omics data. Education Profile B.Sc, Bioengineering, Harbin Institute of Technology, Harbin, China, 2016-2020. M.Sc, Bioengineering, King Abdullah University of Science and Technology, Thuwal, Saudi

Olga Chechekina

Visiting Student, Bio-Ontology Research Group
Olga Chechekina is a visiting student in the Bio-Ontology Research Group under the supervision of Prof. Robert Hoehndorf. Prior to visiting KAUST, she graduated from "Biology and Biotechnology" department at HSE University in 2023, and completed an internship at Genome Institute of Singapore in 2022. Here research interests are in metagenomics, molecular biology and data analysis.