data integration

AI4GH Seminar Series | Predicting Protein Function and Phenotype Associations

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B2 R5220

The amount of available protein sequences is rapidly increasing, mainly as a consequence of the development and application of high ​throughput sequencing technologies in the life sciences.

Novel Computational Methods to Predict Drug–target Interactions Using Graph Mining and Machine Learning Approaches

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B3 L5 R5220

Abstract Computational drug repurposing aims at finding new medical uses for existing drugs. The identification of novel drug-target interactions (DTIs) can be a useful part of such a task. Finding computationally DTIs is a convenient strategy to identify potentially new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer a high false positive prediction rate. Here, we present a comprehensive review of the recent progress in the field of DTI prediction from data-centric and algorithmic-centric perspectives that can help in constructing novel reliable

AI4GH Seminar Series - Machine Learning with Biological Knowledge Graphs

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B2 R5220

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

Mona Alshahrani defends her thesis proposal

1 min read ·
Mona Alshahrani, a Ph.D. candidate under the supervision of Assistant Professor Robert Hoehndorf, defended her thesis proposal on October 10, 2018.

Senay Kafkas

Research Scientist, Bio-Ontology Research Group
Research Interests Senay's research interests include biomedical text/data mining, Natural Language Processing (NLP), and Next Generation Sequencing (NGS) data analysis. Scientific and Professional Membership ​International Society of Computational Biology (ISCB) (2010-present). KAUST Affiliations Bio-Ontology Research Group (BORG).​ Computational Bioscience Research Center (CBRC). Computer, Electrical, and Mathematical Sciences and Engineering (CEMSE). Education Profile Ph.D., Computer Engineering, Eastern Mediterranean University, Cyprus, 2012. M.Sc., Computer Engineering, Eastern

Rawan Olayan

Rawan Olayan obtained both, her MS Degree and PhD Degree from Computer Science at KAUST in 2013 and 2018 respectively. Her master thesis and PhD thesis supervisor was Vladimir Bajic. Under the supervision of her advisor Professor Vladimir Bajic, she completed her thesis on developing novel computational methods to predict drug-target interactions and their functional effects using data integration, graph mining and machine learning approaches. Currently, Rawan is working as a postdoctoral associate in the Carter Lab at the Jackson Laboratory for Genomic Medicine in Farmington, Connecticut. In

Mona Alshahrani

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

Alyaa M Mohamed

Ph.D., Bioscience
Alyaa M Mohamed obtained her Ph.D. degree under the co-supervision of Prof. Xin Gao at Structural and Functional Bioinformatics Group and Prof. Takashi Gojobori at King Abdullah University of Science and Technology (KAUST). Currently, Alyaa is a Postdoc Fellow at University of Calgary, Canada. Research Interests Genome-scale metabolic modeling, Gene expression analysis, Constraint-based modeling. Education Profile Ph.D. in Bioscience, King Abdullah University of Science and Technology, Saudi Arabia, 2012-2018. M.Sc. in Pharmacology, German University in Cairo, Egypt, 2010. ​B.Sc. in Pharmacy

Ahmed El-Roby

Ahmed El-Roby obtained his M.S. degree in Computer Science under the supervision of Professor Panagiotis Kalnis at InfoCloud Research Group (CLOUD) in King Abdullah University of Science and Technology (KAUST). He got his Ph.D. in Computer Science from the David R. Cheriton School of Computer Science at the University of Waterloo. Currently, Ahmed is working as an Assistant Professor at the School of Computer Science at Carleton University in Ottawa, Ontario. Research Interests Ahmed's research interests included Data Integration, Automatic Linking, User Feedback, Artificial Intelligence, and

Adil Salhi

Adil Salhi is a Research Engineer at Prof. Xin Gao's Structural and Functional Bioinformatics Group (SFB) at King Abdullah University of Science and Technology (KAUST). Adil obtained his Ph.D. degree in Computer Science from the University of Manchester, UK. Research Interests Adil's research interests lie in the area of data and text mining of biomedical literature. The extraction of new information from disparate structured data sources, or available information buried in free-form text, requires the deployment of a wide array of tools to overcome the challenges posed by the nature and size