A pair of newly developed models may help better predict outcomes in patients with diffuse large B-cell lymphoma (DLBCL). The ...

Distributed deep learning has emerged as an essential approach for training large-scale deep neural networks by utilising multiple computational nodes. This methodology partitions the workload either ...

Of 372 patients studied, 79.3% and 20.7% were in the completion group and the non-completion group, respectively. The final BERT model achieved average F1 scores of 0.91 and 0.98 for time to ...

Over the past decades, computer scientists have introduced numerous artificial intelligence (AI) systems designed to emulate ...

Physiologically Based Pharmacokinetic Model to Assess the Drug-Drug-Gene Interaction Potential of Belzutifan in Combination With Cyclin-Dependent Kinase 4/6 Inhibitors A total of 14,177 patients were ...

Factoring out nucleotide-level mutation biases from antibody language models dramatically improves prediction of functional mutation effects while reducing computational cost by orders of magnitude.

Consumer wearables have become everyday tools for monitoring sleep and physical activity. Researchers at the Centre for Sleep ...

Understanding how genes are switched on and off in specific cell types remains one of biology's central challenges. While AI ...

Weather forecasts could soon pinpoint individual clouds and tornadoes using AI. A new study reveals how merging artificial intelligence with satellite data may overcome decades-old computing limits, ...

Deep learning is a branch of machine learning based on algorithms that try to model high-level abstract representations of data by using multiple processing layers with complex structures. One of the ...

The deep learning model developed by researchers at the University of Pennsylvania identified severe heart dysfunction far more effectively than traditional AI methods. It also diagnosed 39 cardiac ...