AI in genomics offers transformative opportunities by enhancing drug discovery and personalized medicine through efficient genomic data analysis. Drivers include the surge in genomic data, the focus ...

In the rapidly evolving realm of genetics, the integration of artificial intelligence (AI) has ushered in new perspectives on therapeutic approaches and evolutionary processes. Traditional genetic ...

When the performance of AI models was assessed within stratified patient subgroups, such as only high-grade breast cancers or only MSI-positive tumors, accuracy fell substantially, revealing that the ...

Artificial intelligence tools are increasingly being developed to predict cancer biology directly from microscope images, ...

Plants are constantly exposed to a wide array of biotic and abiotic stresses in their natural environments, posing ...

Objective Cardiovascular diseases (CVD) remain the leading cause of mortality globally, necessitating early risk identification to improve prevention and management strategies. Traditional risk ...

"Medical Genomics" research group at ISTA "Data Science, Machine Learning, and Information Theory" research group at ISTA Al Depope, Jakub Bajzik, Marco Mondelli, and Matthew R. Robinson. 2026. Joint ...

To prevent algorithmic bias, the authors call for multivariable modeling frameworks that jointly incorporate biological sex, genetic ancestry, and gender-related life-course exposures.

Fragments of DNA from long-extinct human relatives still circulate in modern genomes, and in some cases they do more than linger. They actively shape how people survive in extreme environments. The ...

Machine-learning hedge funds surged on the recent jump in precious metals prices, before sidestepping last week's sell-off. Also known as commodity trading advisors (CTAs), the sector notched up one ...

This project implements an advanced Virtual Machine Placement (VMP) optimization system that leverages multi-objective genetic algorithms, machine learning predictions, blockchain technology, and ...

This project focuses on detecting cyber attacks using machine learning techniques. It employs various algorithms to analyze network traffic and identify potential threats in real-time.