About Azza Althagafi Azza Althagafi Ph.D. Student (former), Computer Science Biomedical Informatics artificial intelligence machine learning data mining Azza Althagafi is 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. Professional Profile 2016 to present Graduate Student, KAUST, Thuwal, Saudi Arabia. 2015 - 2016: Teaching Assistant, Taif University, Taif, Saudi Arabia. 2014 – 2015: Volunteer Teaching Assistant, Umm Al-Qura University, Makkah, Saudi Arabia. 2013 – 2016: Assistant Trainer, Mawhiba, The King Abdulaziz and His Companions' Foundation for Giftedness and Creativity, Makkah, Saudi Arabia. Scientific and Events Presented Events Jul 16 - Jul 22, 2023 Prioritizing Causative Genomic Variants by Integrating Molecular and Functional Annotations from Multiple Biomedical Ontologies Azza Althagafi, Ph.D. Student (former), Computer Science Jul 20, 13:00 - 17:00 B2 L5 R5220 The dissertation focuses on developing novel computational methods to improve the diagnosis of patients with rare or complex diseases. By systematically relating human phenotypes resulting from gene function loss or change to gene functions and anatomical/cellular locations, the candidate aims to enhance the prediction and prioritization of disease-causing variants. These methods, leveraging graph-based machine learning and biomedical ontologies, demonstrate significant improvements over existing approaches. The presentation will include a systematic evaluation of the methods, demonstrating their ability to compensate for incomplete data and their applications in biomedicine and clinical decision-making. This research contributes to more effective methods for predicting disease-causing variants and advancing precision medicine, offering promising prospects for improved diagnostics and patient care. Oct 28 - Nov 3, 2018 VSIM: Visualization and Simulation of Genomes for Premarital Testing Azza Althagafi, Ph.D. Student (former), Computer Science Nov 1, 08:00 - 10:00 B3 L5 R5209 bioinformatics machine learning artificial intelligence genomics Abstract Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data. We have developed VSIM an automated and easy to use web application for interpretation and visualization of a variety of genomics data, it identifies the candidate disease variants by referencing to four databases Clinvar, GWAS, DIDA, and PharmGKB, and predicted the pathogenic variants. Moreover, it investigates the attitude towards AI4GH Seminar Series - VSIM: Visualization and Simulation of Genomes for Premarital Testing Azza Althagafi, Ph.D. Student (former), Computer Science Oct 31, 12:00 - 13:00 B2 R5220 bioinformatics machine learning artificial intelligence genomics Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data.
Prioritizing Causative Genomic Variants by Integrating Molecular and Functional Annotations from Multiple Biomedical Ontologies Azza Althagafi, Ph.D. Student (former), Computer Science Jul 20, 13:00 - 17:00 B2 L5 R5220 The dissertation focuses on developing novel computational methods to improve the diagnosis of patients with rare or complex diseases. By systematically relating human phenotypes resulting from gene function loss or change to gene functions and anatomical/cellular locations, the candidate aims to enhance the prediction and prioritization of disease-causing variants. These methods, leveraging graph-based machine learning and biomedical ontologies, demonstrate significant improvements over existing approaches. The presentation will include a systematic evaluation of the methods, demonstrating their ability to compensate for incomplete data and their applications in biomedicine and clinical decision-making. This research contributes to more effective methods for predicting disease-causing variants and advancing precision medicine, offering promising prospects for improved diagnostics and patient care.
VSIM: Visualization and Simulation of Genomes for Premarital Testing Azza Althagafi, Ph.D. Student (former), Computer Science Nov 1, 08:00 - 10:00 B3 L5 R5209 bioinformatics machine learning artificial intelligence genomics Abstract Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data. We have developed VSIM an automated and easy to use web application for interpretation and visualization of a variety of genomics data, it identifies the candidate disease variants by referencing to four databases Clinvar, GWAS, DIDA, and PharmGKB, and predicted the pathogenic variants. Moreover, it investigates the attitude towards
AI4GH Seminar Series - VSIM: Visualization and Simulation of Genomes for Premarital Testing Azza Althagafi, Ph.D. Student (former), Computer Science Oct 31, 12:00 - 13:00 B2 R5220 bioinformatics machine learning artificial intelligence genomics Interpretation and simulation of the large-scale genomics data are very challenging, and currently, many web tools have been developed to analyze genomic variation which supports automated visualization of a variety of high throughput genomics data.
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