Rose Yu has drawn on the principles of fluid dynamics to improve deep learning systems that predict traffic, model the ...

Endogenous intracellular allosteric modulators of GPCRs remain largely unexplored, with limited binding and phenotype data available. This gap arises from the lack of robust computational methods for ...

In a recent study published in the journal Nature Methods, a group of researchers developed a novel method called Ribonucleic Acid (RNA) High-Order Folding Prediction Plus (RhoFold+). This deep ...

In the rapidly advancing field of computational biology, a newly peer-reviewed review explores the transformative role of deep learning techniques in revolutionizing protein structure prediction ...

In the rapidly advancing field of computational biology, a newly peer-reviewed review explores the transformative role of deep learning techniques in revolutionizing protein structure prediction.

AlphaFold3 can perform these calculations because its underlying machine-learning architecture and ... he's been using the tool for predicting the structure of proteins that interact with DNA ...

and used deep learning to study the features of the crystal structure that contribute to their explosiveness. To this end, the researchers also employed a special variational autoencoder using ...

For example, one can use an autoencoder to extract important ... Figure 5 provides a block diagram showing some major deep learning methods to be discussed in the following sections. FIGURE 6. The ...

Deep learning, or artificial neural networks, is a type of machine learning algorithm that can decipher underlying relationships from large volumes of data and has been successfully applied to solve ...