text mining

AI4GH Seminar Series - Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations

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B2 R5220

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.

AI4GH Seminar Series - Large-scale Text and Data Integration for Cancer Genomics Research

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B19 L3 H2

Prof. Rebholz-Schuhmann has long-term experience in semantics driven data analytics research, as well in life sciences as in other domains.

Variant Prioritization in Cancer: Understanding and Prediction of Cancer Driver Genes and Mutations

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B3 R5209

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

Large-scale Text and Data Integration for Cancer Genomics Research

Prof. D, Rebholz-Schuhmann (University Cologne, Germany​)​

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B19 L3 H2

Abstract This talk will give an overview of data and text mining in the life science domain, but will also point out the use of semantic technologies for other domains. Such solutions cover the integration of heterogeneous data sources based on Semantic Web standards (e.g., TCGA, Cosmic, others), the development of smart technologies (e.g., stream processing), graph and literature analytics, and open data initiatives. Application domains are biomedical solutions in cancer genomics and targeted projects together with industrial partners exploiting Semantic Web technologies in general. The

Sara Althubaiti defends her Master's thesis

1 min read ·
Sara Althubaiti, a student under the supervision of Assistant Professor Robert Hoehndorf, defended her Master's thesis on November 5, 2018.

Sarah Alqabbaa

Visiting Student, Computer Science
Sarah Alqabbaa was a visiting student from Effat University ​at Professor Robert Hoehndorf's Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST).

Sara Althubaiti

Ph.D. Student, Computer Science
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

Salwa Al Ahmari

Visiting Student, Bio-Ontology Research Group
Salwa Al Ahmari was a visiting student ​at Professor Robert Hoehndorf's Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST). During her six months (Jan 2016 – Jun 2016) internship at KAUST, she worked on text mining techniques for Ontologies in CBRC.