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 ...

Researchers have developed a hybrid surrogate model for iso-octanol oxidation to iso-octanal that integrates data-driven ...

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.

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 ...

Impact of treatment patterns on clinical outcomes in patients of advanced pancreatic cancer treated with chemotherapy: A large-scale data analysis from real world practice. This is an ASCO Meeting ...

A research team co-led by scientists at the Netherlands Cancer Institute (NKI) and Oncode Institute has developed a deep learning model, PARM (promoter activity regulatory model) that offers up new ...

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

Modern neuroscience has transitioned from small-scale manual observations to a data-intensive field powered by computational innovation. Traditionally ...

CNN architecture summary: The first dimension in all the layers “?” refers to the batch size. It is left as an unknown or unspecified variable within the network architecture so that it can be chosen ...

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

Artificial intelligence (AI) and machine learning (ML) systems have become central to modern data-driven decision-making. They are now widely applied in fields as diverse as healthcare, finance, ...