About Sara Althubaiti Sara Althubaiti Ph.D. Student (former), Computer Science bioinformatics text mining machine learning Ontologies cancer Sara Althubaiti is currently 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. Prior to this, Sara received her master's degree in computer science with a focus on bioinformatics from KAUST in December 2018. Research Interests Sara's research interests include bioinformatics, text mining, ontologies, and cancer. Her research focuses on applying machine learning methods in cancer biology and development specifically in the field of finding driver genes and mutations in Events Presented Events Nov 4 - Nov 10, 2018 Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations Sara Althubaiti, Ph.D. Student (former), Computer Science Nov 5, 10:30 - 12:30 B3 R5209 bioinformatics text mining machine learning Ontologies cancer Abstract Sequencing has identified millions of somatic mutations in human cancers. Identifying and distinguishing cancer driver genes amongst the millions of candidate mutations remains a major challenge. Accurate identification of driver genes and driver mutations is critical for advancing cancer research and personalizing treatment based on accurate stratification of patients. Due to inter-tumor genetic heterogeneity, many driver mutations within a gene occur at low frequencies, which make it challenging to distinguish them from other non-driver mutations. Motivated by these challenges, we
Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations Sara Althubaiti, Ph.D. Student (former), Computer Science Nov 5, 10:30 - 12:30 B3 R5209 bioinformatics text mining machine learning Ontologies cancer Abstract Sequencing has identified millions of somatic mutations in human cancers. Identifying and distinguishing cancer driver genes amongst the millions of candidate mutations remains a major challenge. Accurate identification of driver genes and driver mutations is critical for advancing cancer research and personalizing treatment based on accurate stratification of patients. Due to inter-tumor genetic heterogeneity, many driver mutations within a gene occur at low frequencies, which make it challenging to distinguish them from other non-driver mutations. Motivated by these challenges, we
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