A Spatiotemporal Style Transfer Algorithm for Dynamic Visual Stimulus Generation

Information Science, Computer Science, Artificial Intelligence, Medicine, Neurology,
dynamic visual stimulus   style transfer   deep learning   vision science   spatiotemporal features   artificial vision   neuroscience  

Research Report on the Spatiotemporal Style Transfer Algorithm for Dynamic Visual Stimulus Generation Academic Background The encoding and processing of visual information has been a significant focus in the fields of neuroscience and vision science. With the rapid development of deep learning techniques, investigating the similarities between arti...

Comprehensive Prediction and Analysis of Human Protein Essentiality Based on a Pretrained Large Language Model

Information Science, Artificial Intelligence, Medicine, Medical Laboratory Science, Life Sciences, Bioengineering,
pretrained model   protein essentiality   deep learning   biomedicine   biomarkers   cancer prognosis   microproteins  

Comprehensive Prediction and Analysis of Human Protein Essentiality Based on a Pretrained Large Language Model Academic Background Human Essential Proteins (HEPs) are crucial for individual survival and development. However, experimental methods for identifying HEPs are often costly, time-consuming, and labor-intensive. Additionally, existing compu...

A Deep Learning Approach for Rational Ligand Generation with Toxicity Control

Chemistry, Medicinal Chemistry, Information Science, Artificial Intelligence, Life Sciences, Biochemistry,
deep learning   ligand generation   toxicity control   drug design   computational model   block-based generation   molecular optimization  

Latest Research on Deep Learning Applied to Target Protein Ligand Generation: Proposal and Validation of the DeepBlock Framework Background and Research Problem In the drug discovery process, finding ligand molecules that bind to specific proteins has always been a core objective. However, current virtual screening methods are often limited by the ...

Predicting Crystals Formation from Amorphous Precursors Using Deep Learning Potentials

Materials Science, Inorganic Non-Metallic Materials, Information Science, Computer Science, Artificial Intelligence,
deep learning   amorphous precursors   crystal formation   non-crystalline phases   polymorphs   materials science   energy landscape  

Predicting the Emergence of Crystals from Amorphous Precursors: Deep Learning Empowers Breakthroughs in Materials Science Background Introduction The process of crystallization from amorphous materials holds significant importance in both natural and laboratory settings. This phenomenon is widespread in various processes ranging from geological to ...

Residual-Dense Network for Glaucoma Prediction Using Structural Features of Optic Nerve Head

Information Science, Artificial Intelligence, Medicine, Ophthalmology, Medical Imaging,
glaucoma   residual-dense network   optic nerve head   structural features   deep learning   optic cup   optic disc  

Using Residual Dense Network (RD-Net) for Glaucoma Prediction Based on Structural Features of the Optic Nerve Head Background and Research Purpose Glaucoma is one of the leading causes of blindness worldwide, often referred to as the “silent thief of sight.” It is characterized by the progressive degeneration of the optic nerve head (ONH), resultin...