EDUCATION
CURRENT COURSES
FUNDAMENTAL SKILLS IN BIOINFORMATICS
KAUST. On-campus
The course provides a broad and mainly practical overview of very fundamental skills for the area of bioinformatics. Topics are selected to be relevant for the biologist / biomedical scientist with limited or none background in programming or quantitative analysis. The aim is to support the simultaneous development of quantitative and programming skills for biological and biomedical students. Through the course, the student will develop the necessary practical skills to conduct fundamental data analysis and develop the framework to establish advanced programming and analytical skills in the next courses. A particular aim is to provide the participants with long-term skill on programming and the guidelines for improving their knowledge on it. Read more
UNDERSTANDING AND CONSTRUCTING BIOINFORMATICS PIPELINES
KAUST. On-campus
This course introduces students to the design, implementation, and interpretation of computational pipelines used in genomics. Starting with RNA-seq analysis, participants progressively explore a range of data types—including ATAC-seq, single-cell RNA-seq, multi-omics, and DNA methylation—developing a solid foundation in both the theoretical and practical aspects of pipeline construction. Emphasis is placed on reproducibility, code management (e.g., GitHub), and statistical considerations underlying count data. Finally, It culminates in group projects focused on developing pipelines for specialized data types such as Hi-C, CITE-seq, or CyTOF.
FUNDAMENTAL SKILLS IN BIOINFORMATICS
Coursera. Online
The course provides a broad and mainly practical overview of fundamental skills for bioinformatics (and, in general, data analysis). The aim is to support the simultaneous development of quantitative and programming skills for biological and biomedical students with little or no background in programming or quantitative analysis. Read more
PAST COURSES
COMPUTATIONAL BIOSCIENCE AND MACHINE LEARNING
KAUST. On-campus
The course provides a broad and practical overview of selected techniques and concepts in rapidly developing areas such as bioinformatics, computational biology, systems biology, systems medicine, network biology, synthetic biology, data analytics, predictive modelling, machine learning, and machine intelligence. Topics are selected to be of relevance for the computer scientist, working biologist, computational scientist, and applied investigator (Biotechnology and engineering). Read more
MACHINE LEARNING IN GENOMICS AND HEALTH
KAUST On-campus
Recent progress in machine learning and artificial intelligence is currently transforming genomics, translational medical research, healthcare, and wellness. Huge data-sets are produced at an increasing rate. This include recordings of smart living augmented by sensor devices, medical images, text data in healthcare and social media, and genomics profiling of a range of different biomolecular data. Concurrent with these developments there has over the last 5 years been a stunning production of open source machine learning tools and powerful computational platforms. These advances are currently advancing bioinformatics, computational biology, systems biology, where an area which could be referred to as Digital Medicine in a broad sense is emerging. We expect students with a background in computer science, mathematics, bioscience, and engineering to learn how to use, develop, and to think on how to use ML/AI techniques in what can broadly be referred to as Digital Technologies for Medicine and Health. Read more