This figure shows how the STAIG framework can successfully identify spatial domains by integrating image processing and contrastive learning to analyze spatial transcriptomics data effectively.

Overview: The choice of deep learning frameworks increasingly reflects how AI projects are built, from experimentation to ...

Biological tissues are made up of different cell types arranged in specific patterns, which are essential to their proper functioning. Understanding these spatial arrangements is important when ...

(a) An illustrative map for Crack-Net, including the initial features, looping solver for data generation, model training, and prediction performance. (b) The Crack-Net architecture, which utilizes ...

An international research team developed CyberSentry, a software framework using advanced deep learning and optimization ...

Gathering and processing this data is time consuming and expensive, limiting the models' use to areas with long-term data records and high-powered computers. To overcome these limitations, the ...

COMET, a novel machine learning framework, integrates EHR data and omics analyses using transfer learning, significantly enhancing predictive modeling and uncovering biological insights from small ...

A new learning paradigm developed by University College London (UCL) and Huawei Noah’s Ark Lab enables large language model (LLM) agents to dynamically adapt to their environment without fine-tuning ...

Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...