Scientists at Shaanxi Normal University have developed an AI-powered dual-channel model that predicts miRNA–drug interactions with up to 96% accuracy—validated on public datasets and real-world drugs—to accelerate and economize the discovery of novel therapeutic targets.

Researchers from Nanjing University and UC Berkeley have unveiled a clustering-based reinforcement learning framework that balances novelty and reward to accelerate and enhance AI exploration across robotics, gaming, and real-world applications.

Researchers have developed a self-tuning AI framework that dynamically filters noisy graph data to boost reliability and accuracy across industries from healthcare to finance.

Researchers from Jilin University and the University of North Carolina have developed an energy-efficient, stability-boosting data-offloading method that uses advanced optimization algorithms to slash delays and power use in mobile crowdsensing, paving the way for smoother, greener smart-city services.

Researchers at Tongji University and the Shanghai AI Lab show that graph-based neural networks can uncover hidden money-laundering rings and collusion networks in financial transactions far more effectively than traditional methods, offering a clear roadmap for real-world implementation and stronger fraud defenses across banking, insurance, and regulatory systems.

A review from Xidian University shows that advanced computational algorithms—from neural networks and matrix methods to recommendation engines and text-mining—can reliably predict novel uses for existing drugs, offering a faster, lower-cost, and lower-risk path to discovering new therapies.

Researchers from Jinan University, Huawei, and ByteDance have developed LTAA-FGAC, an innovative authentication system that balances user privacy with public traceability and fine-grained access control to enhance digital security and accountability.

An international team of researchers has developed LLaVA-Endo, a powerful new AI tool that helps doctors more accurately diagnose digestive diseases by combining visual and language understanding during gastrointestinal endoscopies.

Researchers at Nanjing University have developed a new training framework and benchmark that dramatically enhance how AI communicates and coordinates with humans in unpredictable, real-world scenarios, paving the way for safer, more efficient collaboration across various industries.

Researchers have developed an innovative AI approach that embeds causal reasoning into offline reinforcement learning, enabling autonomous systems like driverless cars and medical support devices to make safer, more reliable decisions by accurately discerning true cause-and-effect relationships in historical data.