Monday, April 06, 2020, 19:30
- 21:30
https://kaust.zoom.us/j/858990591
Contact Person
We developed and expanded novel methods for representation learning, predicting protein functions and their loss of function phenotypes. We use deep neural network algorithm and combine them with symbolic inference into neural-symbolic algorithms. Our work significantly improves previously developed methods for predicting protein functions through methodological advances in machine learning, incorporation of broader data types that may be predictive of functions, and improved systems for neural-symbolic integration. The methods we developed are generic and can be applied to other domains in which similar types of structured and unstructured information exist. In future, our methods can be applied to prediction of protein function for metagenomic samples in order to evaluate the potential for discovery of novel proteins of industrial value.  Also our methods can be applied to the prediction of loss of function phenotypes in human genetics and incorporate the results in a variant prioritization tool that can be applied to diagnose patients with Mendelian disorders.
Tuesday, March 03, 2020, 10:00
- 11:30
Building 9, Level 2, Hall 2, Room 2325
In my research I aim to understand how formalized knowledge bases can be used to systematically structure and integrate biological knowledge, and how to utilize these formalized knowledge bases as background knowledge to improve scientific discovery in biology and biomedicine.  To achieve these aims, I develop methods for representing, integrating, and analyzing data and knowledge with the specific aim to make the combination of data and formalized knowledge accessible to data analytics and machine learning in bioinformatics. Biomedicine, and life sciences in general, are an ideal domain for knowledge-driven data analysis methods due to the large number of formal knowledge bases that have been developed to capture the broad, diverse, and heterogeneous data and knowledge.
Monday, January 20, 2020, 08:00
- 17:00
Building 19, Level 2, Hall 1
Computational Bioscience Research Center at King Abdullah University of Science and Technology is pleased to announce the KAUST Research Conference on Digital Health 2020. To see the agenda of the conference Digital Health 2020 visit agenda page. To view ​frequently asked questions, visit FAQ page.
Dr. Michel Dumontier, Distinguished Professor of Data Science at Maastricht University, The Netherlands
Monday, November 04, 2019, 12:00
- 13:00
Building 9, Level 2, Hall 1, Room 2322
Contact Person
In this talk, I will discuss our work to create computational standards, platforms, and methods to wrangle knowledge into simple, but effective representations based on semantic web technologies that are maximally FAIR - Findable, Accessible, Interoperable, and Reuseable - and to further use these for biomedical knowledge discovery. But only with additional crucial developments will this emerging Internet of FAIR data and services enable automated scientific discovery on a global scale.
Monday, September 23, 2019, 09:00
- 04:00
Graz, Austria
Contact Person
KAUST Assistant professor Robert Hoehndorf will be giving a keynote presentation at the 4th International Workshop on Ontology Modularity, and Evolution @ JOWO 2019, Graz, Austria.
Monday, February 04, 2019, 13:00
- 14:00
B19, Level 3, Hall 2
Contact Person
Prof. Rebholz-Schuhmann has long-term experience in semantics driven data analytics research, as well in life sciences as in other domains.
Adeeb Noor, King Abdulaziz University
Sunday, December 02, 2018, 12:30
- 13:30
B 2, Room 5220
Contact Person
Contemporary drug-drug interactions  (DDIs)  research offers a  clinically impactful opportunity to identify  DDIs prior  to their occurrence;  however,  the clinical utility of current  DDI  identification systems have,  to date,  been greatly limited by the inability to accurately and concisely identify their pathway of interaction for the vast lists of identified computationally and clinically valid DDIs.
Sunday, November 25, 2018, 12:00
- 13:00
B 2, Room 5220
Contact Person
Malaria kills nearly one-half million people a year and over 1 billion people are at risk of becoming infected by the parasite. Plasmodial infections are difficult to treat for a myriad of reasons, but the ability of the organism to remain latent in hosts and the complex life cycles greatly contributed to the difficulty in treat malaria.
Sunday, November 18, 2018, 12:00
- 13:00
B2, Room 5220
Contact Person
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.
Wednesday, October 31, 2018, 12:00
- 13:00
Building 2 Room 5220
Contact Person
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.
Wednesday, October 17, 2018, 12:00
- 13:00
Building 2, Room 5220
Contact Person
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.
Wednesday, October 10, 2018, 12:00
- 13:00
Building 2, Room 5220
Contact Person
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.
Wednesday, July 25, 2018, 08:00
- 13:00
B3 Room 5220
Contact Person
We will investigate how novel AI technologies, including progress in machine learning, knowledge representation and reasoning can be applied to improving diagnosis and ​treatment of cancer in the era of genomic medicine.