Computer science

Knowledge-based methods in life sciences

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B9 L2 H2 R2325

In my research I aim to understand how formalized knowledge bases can be used to systematically structure and integrate biological knowledge, and how to utilize these formalized knowledge bases as background knowledge to improve scientific discovery in biology and biomedicine. To achieve these aims, I develop methods for representing, integrating, and analyzing data and knowledge with the specific aim to make the combination of data and formalized knowledge accessible to data analytics and machine learning in bioinformatics. Biomedicine, and life sciences in general, are an ideal domain for knowledge-driven data analysis methods due to the large number of formal knowledge bases that have been developed to capture the broad, diverse, and heterogeneous data and knowledge.

CBRC Dual Seminar - Analysis of COVID-19 Severity - DeepSVP computational method

Sakhaa Al-Saedi, PhD Student; Azza Althagafi, PhD Student

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KAUST

Sakhaa Al-Saedi: We conduct a systematic genetic analysis of risk variants related to increasing the severity of COVID-19. It leads to a better understanding of its genetic basis and identifies the host genes to be targeted to tackle the COVID-19 pandemic and reduce its death toll. Azza Althagafi: We developed DeepSVP, a computational method to prioritize structural variants involved in genetic diseases by combining genomic and gene functions information. DeepSVP significantly improves the success rate of finding causative variants in several benchmarks and can identify novel pathogenic structural variants in consanguineous families.

Disease researchers have a way with words

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Specialized word search method sheds light on phenotypic similarity of diseases.

Algorithm scours datasets to diagnose medical mysteries

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A new tool uses genetic and clinical information to find the root cause of unexplained illnesses.

Database to support infectious disease research

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Linking disease pathogens to clinical signs and symptoms through a database could support research into the molecular mechanisms of infectious diseases.

Symbol of change for AI development

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Bridging the knowledge gap in artificial intelligence requires an embedding function that helps step between different types of "thinking."

Algorithm turns cancer gene discovery on its head

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A method for finding genes that spur tumor growth takes advantage of machine learning algorithms to sift through reams of molecular data collected from studies of cancer cell lines, mouse models and human patients.

MS Thesis Defense: Embedding Ontologies using Category Theory Semantics by Fernando Zhapa Camacho

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Fernando Patricio Zhapa Camacho an M.S./Ph.D. candidate in the KAUST Bio-Ontology research group under the supervision of Professor Robert Hoehndorf successfully defended his thesis "Embedding Ontologies using Category Theory Semantics".

MS Thesis Defense | Learning 3D structures for protein function prediction

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Student Nurul Muttakin from the Bio-Ontology Research Group (BORG) under the supervision of Assoc. Prof. Robert Hoehndorf successfully defends his Masters thesis. He presented his research on the topic " Learning 3D structures for protein function prediction".

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

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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.

PhD Dissertation Defense | Prioritizing Causative Genomic Variants by Integrating Molecular and Functional Annotations from Multiple Biomedical Ontologies

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CBRC PhD Student Azza Althagafi successfully defends her dissertation under the supervision of Associate Professor Robert Hoehndorf. She has been a member of the Bio-Ontology Research Group (BORG) since 2016. Her research interest lies in the crossroads of computer science and biology, leveraging AI techniques to explore the human genome's intricacies and its link to various diseases.