Natasa Przulj at the Barcelona Supercomputing Center is developing an AI framework that fuses multi-omic data to improve precision medicine. The framework uses graph-regularized non-negative matrix tri-factorization (NMTF) and network science algorithms for patient stratification, biomarker prediction, and drug repurposing. It is applied to diseases like cancer, Covid-19, and Parkinson's. Why it matters: This research can enable more personalized and effective treatments by leveraging complex biological data to understand disease mechanisms and tailor therapies.
MBZUAI's Professor Le Song is developing an AI-driven simulation to model the human body at societal, organ, tissue, cellular, and molecular levels. The goal is to reduce the time and cost associated with bringing new medicines to market by removing the need for wet lab biological research. Song aims to create a comprehensive model using machine learning. Why it matters: This research could revolutionize drug discovery in the region by accelerating the development process and reducing reliance on traditional research methods.
Shahar Harel, Head of AI at Quris, presented a BIO-AI approach to drug safety assessment using a 'patient-on-a-chip' platform. This platform simulates the human body and generates high-frequency microscopy and biochemical data on drug interactions, considering patient genomics and ethnicity. The data is used to train multimodal deep learning models to predict drug safety and provide patient-specific recommendations. Why it matters: This approach offers a potential alternative to animal models, promising faster and more personalized drug development while reducing safety concerns.
A KAUST alumnus presented research on using large language models for complex disease modeling and drug discovery. LLMs were trained on insurance claims of 123 million US people to model diseases and predict genetic parameters. Protein language models were developed to discover remote homologs and functional biomolecules, while RNA language models were used for RNA structure prediction and reverse design. Why it matters: This work highlights the potential of LLMs to accelerate computational biology research and drug development, with a KAUST connection.
MBZUAI researchers have developed MorphDiff, a diffusion model that predicts cell morphology from gene expression data. MorphDiff uses the transcriptome to generate realistic post-perturbation images, either from scratch or by transforming a control image. The model combines a Morphology Variational Autoencoder (MVAE) with a Latent Diffusion Model, enabling both gene-to-image generation and image-to-image transformation. Why it matters: This could significantly accelerate drug discovery and biological research by allowing scientists to preview cellular changes before conducting experiments.
KAUST is joining universities worldwide to expedite licensing for COVID-19 related technologies. KAUST researchers are focusing on developing rapid diagnostic platforms, genomic analyses, and tools to track the virus's spread, collaborating with Saudi healthcare stakeholders. By signing the AUTM COVID-19 Licensing Guidelines and adopting the COVID-19 Technology Development Framework, KAUST will offer royalty-free, time-limited, non-exclusive licenses during and after the pandemic. Why it matters: This initiative facilitates quicker development and broader access to essential technologies for combating COVID-19 in Saudi Arabia and the Middle East.
Khaled Alsayegh at the King Abdullah International Medical Research Center is creating a Saudi Stem Cell Donor Registry, with 80,000 potential donors identified. The aim is to identify universal donors, reprogram their cells into induced pluripotent stem (iPS) cells, and create a gene bank for matched tissue transplants. Alsayegh is collaborating with Jesper Tegnér at KAUST to create pacemaker cells using single-cell RNA sequencing. Why it matters: This initiative could revolutionize precision medicine in KSA by providing readily available, matched cells for transplants, reducing the need for patient-specific reprogramming and improving treatment outcomes.
KAUST researchers are analyzing the SARS-CoV-2 genome to identify potential targets for treatment and vaccine development. They are using the KAUST Metagenome Analysis Platform (KMAP) and the university's supercomputer to compare and analyze genomic data. The research focuses on identifying key genes for detection and treatment of COVID-19. Why it matters: This research contributes to the global effort to combat the pandemic and highlights KAUST's capabilities in genomic data analysis and computational bioscience.