AI for Complex Disease Analysis and Risk Stratification

QCRI Precision Health Research

Advancing Precision Health Through Artificial Intelligence and Multi-Omics Research

The QCRI Precision Health team is dedicated to advancing precision medicine through the integration of artificial intelligence (AI), genomics, and multi-modal health data. Our research focuses on accelerating novel biological discoveries and developing highly accurate predictive models for complex diseases, with a particular emphasis on Middle Eastern and Qatari populations.

By leveraging state-of-the-art AI and machine learning methodologies, our team integrates diverse health data modalities — including whole-genome sequencing, metabolomics, proteomics, medical imaging, and electronic health records (EHRs) — to uncover disease mechanisms, identify clinically relevant biomarkers, and improve disease risk prediction and patient stratification.

Our work contributes to the growing field of population-scale precision health and supports the development of more personalized and equitable healthcare solutions for underrepresented populations.


Research Focus Areas

Multi-Modal Precision Health Analytics

We develop and apply advanced computational and AI-driven approaches to integrate large-scale biological and clinical datasets, enabling:

  • Discovery of novel disease-associated biomarkers
  • Identification of genetic and molecular disease mechanisms
  • Development of predictive and polygenic risk models
  • Precision prevention and personalized treatment strategies
  • Population-specific health insights for Middle Eastern ancestries

Disease Areas of Interest

Our research addresses diseases with high prevalence and public health impact in Qatar and the Middle East, including:

  • Cardiometabolic Diseases
    • Coronary Artery Disease
    • Type 2 Diabetes
    • Atrial Fibrillation
    • Hypertension
    • Obesity
    • Electrocardiographic (ECG) Traits
  • Cancer Genomics and Molecular Oncology
  • Complex Chronic Diseases Influenced by Genetic and Environmental Factors

Data Resources and Platforms

Our research is primarily powered by nationally significant precision health initiatives, including:

  • Qatar Precision Health Institute (QPHI)
  • Qatar Biobank (QBB)
  • Qatar Genome Program (QGP)

In addition, we collaborate with and utilize international population-scale datasets such as:

  • UK Biobank
  • All of Us Research Program
  • Other global genomic and clinical data resources

These datasets provide access to large-scale genomic, phenotypic, imaging, and longitudinal clinical information essential for precision medicine research.


Scientific Contributions and Impact

The QCRI Precision Health team has produced high-impact scientific contributions published in internationally recognized journals, including:

  • The Lancet Oncology
  • Immunity
  • Circulation: Genomic and Precision Medicine
  • Journal of the American Heart Association
  • Genome Research
  • Additional leading journals in genomics, cardiovascular medicine, oncology, and computational biology

Our research has contributed to advancing the understanding of disease biology in underrepresented populations and has strengthened the global representation of Middle Eastern genomic data in precision medicine research.


Methodological Expertise

  • Artificial Intelligence and Machine Learning
  • Deep Learning for Healthcare
  • Multi-Omics Data Integration
  • Polygenic Risk Score Development
  • Statistical Genetics and Genomic Analysis
  • Rare Variant Association Studies
  • Biomedical Data Mining
  • Predictive Modeling and Clinical Risk Stratification
  • Electronic Health Record Analytics
  • Population Genomics

Awarded Grants and Funded Projects

Path to Precision Medicine – 6th Cycle

Project: Polygenic, Metabolic, and Clinical Risk Score Utility for Cardiometabolic Traits in Middle Eastern Populations
Lead PI: Dr. Mohamad Saad

Qatar Precision Health Institute Research Program

Project: Multimodal AI-based Analysis of Genomics, Medical Imaging, and Electronic Medical Records in the Middle East: Insights from the Qatar Precision Health Institute Dataset
Lead PI: Dr. Mohamad Saad

Qatar Precision Health Institute Research Program

Project: Association of Rare Variants in Protein-Coding and Long Non-Coding RNA Genes with Cardiometabolic Traits and Cancer
Co-Lead PI: Dr. Mohamad Saad


Collaborations and Partnerships

The QCRI Precision Health team actively collaborates with leading national and international institutions to advance precision medicine research and innovation.

Local Collaborators

  • Qatar Biomedical Research Institute (QBRI)
  • Qatar Precision Health Institute (QPHI)
  • Weill Cornell Medicine-Qatar
  • Hamad Medical Corporation (HMC)
  • Sidra Medicine
  • Qatar Foundation research entities and clinical partners

International Collaborators

  • Queen Mary University of London
  • Mayo Clinic
  • Marshall University
  • University of Washington
  • Additional academic and clinical research centers worldwide

Vision

Our vision is to work locally to establish globally impactful precision health solutions that leverage AI and multi-omics technologies to improve healthcare outcomes, accelerate translational discoveries, and advance equitable precision medicine for diverse populations worldwide.

People

Dr. Mohamad Saad

Senior Scientist

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Dr. Mohamed M. El-Shrif

Software Engineer

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Khalid M. Kunji

Software Engineer

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Abdullah Shaar

Research Assistant

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Parvathy premkumar

RESEARCH ASSISTANT

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AMNA M.SALIM

RESEARCH ASSISTANT

TAMARA SAID EL ARTAH

RESEARCH ASSISTANT

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AREEBA IRFAN

PHD STUDENT

Asma saeed

PHD STUDENT

Publications

Comprehensive Analysis of Rare Variants Associated with Genetic Predisposition to Non-BRCA Familial Breast Cancer Among Arabs

Ehsan Ullah, Hikmat Abdel-Razeq, Sana Bentebbal, Abdullah Shaar, Nehad Alajez, Mohamad Saad, Julie V. Decock Clinical Cancer Research (2025)

Genome‐Wide Association Study for Resting Electrocardiogram in the Qatari Population Identifies 6 Novel Genes and Validates Novel Polygenic Risk Scores

Nahin Khan, Abdullah Shaar, Khalid Kunji, Atlas Khan, Mohamed Elshrif, Mohammed Bashir, Mohammed Thamer Ali, Ayman Al Haj Zen, Krzysztof…

PopMLvis: a tool for analysis and visualization of population structure using genotype data from genome-wide association studies

Mohamed Elshrif , Keivin Isufaj , Khalid Kunji , Mohamad Saad BMC Bioinformatics (2024)

Genetic predisposition to cancer across people of different ancestries in Qatar: a population-based, cohort study

Mohamad Saad , Younes Mokrab , Najeeb Halabi , Jingxuan Shan , Rozaimi Razali , Khalid Kunji , Najeeb Syed…