Leveraging statistical methods and computational tools

The field of human genetics is well advanced , and many statistical methods and computational tools are utilised daily in the medical field , especially in the emerging clinical care model , precision health . These methods can range from simple statistical analysis to more sophisticated Machine Learning methods , such as Deep Learning , where medical experts can extract and predict valuable insights from large data sets .

Electronic health records ( EHR ) and genetic data-informed precision health can help quantify risks for disease in an individual and inform personalised treatment . This capability is a result of years of research and thousands of genome-wide association studies , through many international consortia and programmes . One example is the US National Institute of Health ’ s All of Us research programme , which welcomes patients from all backgrounds to create a diverse database across ethnicities , gender and ages . Qatar Biobank , Qatar Genome Programme and UK Biobank are other important examples focusing on Middle Easterners and Europeans , respectively .

Learnings from these programmes can be integrated into clinical practice across a range of diseases such as coronary heart diseases – where it has been identified that people with certain genetic values are more likely to develop coronary heart disease . As a result , precision health experts can look back at patient and family EHR history , including their genetic makeup and work with doctors to prescribe personalised therapies .

In today ’ s rapidly evolving world , the intersection of advanced technologies and healthcare is transforming the landscape of medical research , diagnosis and treatment . In this article by Dr Mohamad Saad , Senior Research Scientist , Centre for Artificial Intelligence , Qatar Computing Research Institute ( QCRI ) at the Qatar Foundation , we hear of the opportunities to leverage these cutting-edge innovations to explore the possibilities of personalised medicine , biostatistics research and the application of Artificial Intelligence ( AI ).

Another application of statistical tools is in pharmacogenomics , where several genetic markers have been found to be associated with the response to treatments such as immunotherapy to treat cancer . People harbouring some genetic variants are likely to have severe and sometimes lethal side effects of drugs and treatments . As a result , this sort of genetic data can guide clinicians to set prevention plans for patients and ensure the most efficient treatment is being administered .

Enhancing drug discovery and development

Analytical tools of large datasets can not only improve diagnosis and therapeutic strategies but improve the development of the treatment itself . Drug discovery and development is no exception .

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