In the life sciences, extensive knowledge of cell biology provides an opportunity to design visible neural networks (VNNs) that couple the ... We guided the deep neural network structure using ...

Also, RNA secondary structure prediction can be achieved by classifying each base pair’s status (pair or not) (Table 1). A deep learning model is typically thought of as a “black box” containing ...

Therefore, an efficient protein secondary structure predictor is of importance especially ... In this paper, a reductive deep learning model MLPRNN has been proposed to predict either 3-state or ...

This function can tune and adjust itself, over and over at unimaginable levels of complexity, in order to “learn” precisely how a protein sequence mathematically relates to its structure. AlQuraishi ...

This deep learning ... like Assisted Model Building with Energy Refinement (AMBER) resolved structural inaccuracies, ensuring biologically valid predictions. RhoFold+'s structure module uses ...

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 review explores the transformative role of deep learning ... structure prediction. Multimodal prediction: The latest AlphaFold 3 model ...

In general, almost everything we know about neurons, such as their structure, types, and interconnectivity, has been left out of deep learning models ... and could eventually be a component of a ...