City of Hope is using generative artificial intelligence to create operational efficiency, enable AI-driven patient personalization, improve access to clinical trials and empower breakthrough research

This study published in Engineering offers new insights into the impact of alcoholic liver disease (ALD) on post-transplant outcomes in patients with hepatocellular carcinoma (HCC) and hepatitis B virus (HBV) infection. Researchers found that ALD significantly increases the risk of HBV reactivation and worsens survival outcomes following liver transplantation. Using advanced machine learning techniques, the study identified key metabolic factors associated with HBV reactivation and developed a novel risk stratification index to better predict patient outcomes. The findings highlight the importance of considering ALD in the management of liver transplant recipients with HBV-related HCC.

Researchers have developed a new hybrid earthquake early warning system called HEWFERS, which leverages advanced machine learning techniques and seismological principles to provide real-time predictions of ground shaking intensity. The system integrates a domain-informed variational autoencoder, a feed-forward neural network, and Gaussian process regression to offer both on-site and regional early warnings. Validated with a large database of ground motion records, HEWFERS demonstrates enhanced accuracy and reliability in seismic risk mitigation.

A multinational team of researchers, co-led by the Garvan Institute of Medical Research, has developed and tested a new AI tool to better characterise the diversity of individual cells within tumours, opening doors for more targeted therapies for patients.

Abu Dhabi, UAE – Jun 24 – Insilico Medicine(“Insilico”), a clinical-stage generative artificial intelligence (AI)-driven drug discovery company, and United Arab Emirates University (“UAEU”), a leading public university and premier academic institution in the UAE and the Middle East, have signed a Memorandum of Understanding (MoU) to advance the development of local scientific talents and drive research collaborations within the nation’s rapidly growing biotechnology sector. 

Predicting cognitive abilities from brain imaging has long been a central goal in cognitive neuroscience. Narun Pat and colleagues integrated multiple MRI modalities through a technique called stacking. Using brain imaging at age 45, the model predicted childhood cognitive scores (ages 7, 9, and 11) with a .52 Pearson’s correlation—indicating a substantial degree of predictive accuracy. 

A new study has introduced a machine learning-based approach to improve the reliability of Global Navigation Satellite System (GNSS) ambiguity resolution, a critical step for achieving high-precision positioning.