About Mona Alshahrani Mona Alshahrani Ph.D. (former), Computer Science machine learning human bioinformatics data integration network-based approaches Mona Alshahrani obtained her Ph.D. degree in Computer Science under the supervision of Professor Robert Hoehndorf at the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST). Research Interests Machine Learning, human bioinformatics, data integration. The combination of neuro-symbolic methods over ontologies and semantic RDF graphs with statistical methods. Their applications in data analysis and features representations in several biomedical applications such as phenotypes prediction, drug repurposing, and others. I'm also interested in network Events Presented Events Oct 21 - Oct 27, 2018 AI4GH Seminar Series - Machine Learning with Biological Knowledge Graphs Mona Alshahrani, Ph.D. (former), Computer Science Oct 24, 12:00 - 13:00 B2 R5220 bioinformatics data integration machine learning knowledge graphs human health Abstract Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval, and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. We develop novel methods for feature learning on biological knowledge graphs and apply them to the prediction of edges in the knowledge graph representing problems of finding candidate genes of diseases, or drugs
AI4GH Seminar Series - Machine Learning with Biological Knowledge Graphs Mona Alshahrani, Ph.D. (former), Computer Science Oct 24, 12:00 - 13:00 B2 R5220 bioinformatics data integration machine learning knowledge graphs human health Abstract Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval, and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. We develop novel methods for feature learning on biological knowledge graphs and apply them to the prediction of edges in the knowledge graph representing problems of finding candidate genes of diseases, or drugs
Engage ORCID ShareClipboard Related Sites Structural and Functional Bioinformatics (SFB) Bio-Ontology Research Group (BORG) Computer Science (CS) Related Content Articles 3 Projects 2 Events 1 Related Links Publications list on ORCID Also view list of Publications on KAUST Repository Also view list of Publications on Google Scholar
