Research Overview

​​​​Big Data analytics in life sciences

CBRC works on problems of data integration and its application to life sciences relying on Big Data analytics solutions with a focus on three domains: 

  • Human Health and Medicine,
  • Metagenomics, and
  • Biotechnology. 

In the Human Health and Medicine domain, of particular interest are solutions for discovery of the new uses of existing drugs, so-called drug repositioning; solutions for identification of diagnostic biomarkers based on NGS and epigenetic information with specific interest in cancers and cardiovascular diseases; solutions for specific problems in personalised medicine where the full advantage of the information from individual genome variation is taken into account; and efficient solutions for medical informatics and specific personalized medicine problems.


Metagenomics, computational and experimental biotechnology

CBRC aims at developing:

  • Infrastructure for deep analyses of metagenomic samples.
  • Infrastructure for the experimental framework, bioinformatics, and computational modeling, aiming to identify novel molecules useful for industrial, medical, cosmetic, and food industries relevant within the Saudi Arabian and broader contexts, and for a deeper understanding of the molecular functioning of cells.
  • Infrastructure for developing and 3D/4D printing of artificial tissues and organoids for potential use in medicine.


​Research Themes

Bioinformatics Software and Knowledgebases Development of novel solutions and technologies for bioinformatics domain addressing the issues of Big Data, Information Integration, and domain-specific knowledgebases.
AI/ML/DL & HPC Bioinformatics Solutions Development of new high-performance computing models, as well as machine learning models including deep learning ones, in Computational Biology and Bioinformatics to address the huge volume data and information available in the biomedical domain.
Next Generation Knowledge Mining Technologies This program develops methods and technologies to extract efficiently associations between entities from free text and data repositories in the domain of life science and beyond.