About Fatima Zohra Smaili Fatima Zohra Smaili Ph.D. Student, Computer Science protein function prediction Fatima Zohra Smaili is a Ph.D. student at the Structural and Functional Bioinformatics Group (SFB) under the supervision of Professor Xin Gao at King Abdullah University of Science and Technology (KAUST). Research Interests Fatima's research interests include protein function prediction based on sequence and structure features. Education Profile MS, Computer Science, KAUST, Thuwal, Saudi Arabia, 2016 BS, Computer Science, AUI, Morocco, 2014 Events Presented Events Aug 30 - Sep 5, 2020 Machine Learning Models for Biomedical Ontology Integration and Analysis Fatima Zohra Smaili, Ph.D. Student, Computer Science Sep 3, 16:00 - 17:00 KAUST Biological knowledge is widely represented in the form of ontologies and ontology-based annotations. The structure and information contained in ontologies and their annotations make them valuable for use in machine learning, data analysis and knowledge extraction tasks. In this thesis, we propose the first approaches that can exploit all of the information encoded in ontologies, both formal and informal, to learn feature embeddings of biological concepts and biological entities based on their annotations to ontologies by applying transfer learning on the literature. To optimize learning that combines ontologies and natural language data such as the literature, we also propose a new approach that uses self-normalization with a deep Siamese neural network to improve learning from both the formal knowledge within ontologies and textual data. We validate the proposed algorithms by applying them to generate feature representations of proteins, and of genes and diseases. Nov 18 - Nov 24, 2018 AI4GH Seminar Series - Vector Representation of Biological Entities Based on Ontologies and Their Annotations Fatima Zohra Smaili, Ph.D. Student, Computer Science Nov 18, 12:00 - 13:00 B2 R5220 machine learning data analysis 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. AI4GH - Vector Representation of Biological Entities Based on Ontologies and Their Annotations Fatima Zohra Smaili, Ph.D. Student, Computer Science Nov 18, 12:00 - 13:00 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.
Machine Learning Models for Biomedical Ontology Integration and Analysis Fatima Zohra Smaili, Ph.D. Student, Computer Science Sep 3, 16:00 - 17:00 KAUST Biological knowledge is widely represented in the form of ontologies and ontology-based annotations. The structure and information contained in ontologies and their annotations make them valuable for use in machine learning, data analysis and knowledge extraction tasks. In this thesis, we propose the first approaches that can exploit all of the information encoded in ontologies, both formal and informal, to learn feature embeddings of biological concepts and biological entities based on their annotations to ontologies by applying transfer learning on the literature. To optimize learning that combines ontologies and natural language data such as the literature, we also propose a new approach that uses self-normalization with a deep Siamese neural network to improve learning from both the formal knowledge within ontologies and textual data. We validate the proposed algorithms by applying them to generate feature representations of proteins, and of genes and diseases.
AI4GH Seminar Series - Vector Representation of Biological Entities Based on Ontologies and Their Annotations Fatima Zohra Smaili, Ph.D. Student, Computer Science Nov 18, 12:00 - 13:00 B2 R5220 machine learning data analysis 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.
AI4GH - Vector Representation of Biological Entities Based on Ontologies and Their Annotations Fatima Zohra Smaili, Ph.D. Student, Computer Science Nov 18, 12:00 - 13:00 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.
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