Center for Applied and Translational Genomics (CATG)

Permanent URI for this communityhttps://repository.mbru.ac.ae/handle/1/1940

Browse

Browsing Center for Applied and Translational Genomics (CATG) by Author "Alkhnbashi, Omer S"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    Publication
    Genomics of rare diseases in the Greater Middle East.
    (2025-02-03) Chekroun, Ikram; Almarri, Mohamed A; Uddin, Mohammed; Alkhnbashi, Omer S; Ali, Fahad R; Alsheikh-Ali, Alawi; Abou Tayoun, Ahmad N
    The Greater Middle East (GME) represents a concentrated region of unparalleled genetic diversity, characterized by an abundance of distinct alleles, founder mutations and extensive autozygosity driven by high consanguinity rates. These genetic hallmarks present a unique, yet vastly untapped resource for genomic research on Mendelian diseases. Despite this immense potential, the GME continues to face substantial challenges in comprehensive data collection and analysis. This Perspective highlights the region's unique position as a natural laboratory for genetic discovery and explores the challenges that have stifled progress thus far. Importantly, we propose strategic solutions, advocating for an all-inclusive research approach. With targeted investment and focused efforts, the latent genetic wealth in the GME can be transformed into a global hub for genomic research that will redefine and advance our understanding of the human genome.
  • Thumbnail Image
    Publication
    Optimizing Large Language Models for Arabic Healthcare Communication: A Focus on Patient-Centered NLP Applications
    (2024) Alkhnbashi, Omer S
    Recent studies have highlighted the growing integration of Natural Language Processing (NLP) techniques and Large Language Models (LLMs) in healthcare. These technologies have shown promising outcomes across various healthcare tasks, especially in widely studied languages like English and Chinese. While NLP methods have been extensively researched, LLM applications in healthcare represent a developing area with significant potential. However, the successful implementation of LLMs in healthcare requires careful review and guidance from human experts to ensure accuracy and reliability. Despite their emerging value, research on NLP and LLM applications for Arabic remains limited particularly when compared to other languages. This gap is largely due to challenges like the lack of suitable training datasets, the diversity of Arabic dialects, and the language’s structural complexity. In this study, a panel of medical experts evaluated responses generated by LLMs, including ChatGPT, for Arabic healthcare inquiries, rating their accuracy between 85% and 90%. After fine tuning ChatGPT with data from the Altibbi platform, accuracy improved to a range of 87% to 92%. This study demonstrates the potential of LLMs in addressing Arabic healthcare queries especially in interpreting questions across dialects. It highlights the value of LLMs in enhancing healthcare communication within the Arabic-speaking world and points to a promising area for further research. This work establishes a foundation for optimizing NLP and LLM technologies to achieve greater linguistic and cultural adaptability in global healthcare settings.