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...

A Simulated Annealing Algorithm for Randomizing Weighted Networks

Information Science, Computer Science, Artificial Intelligence, Medicine, Neurology,
weighted networks   simulated annealing   connectomics   network randomization   brain network organization   strength sequence   rewiring algorithms  

Research on Weighted Network Randomization Based on Simulated Annealing Algorithm Background Introduction In the field of neuroscience, connectomics is an important branch for studying the structure and function of brain neural networks. With the development of modern imaging technologies, researchers are able to acquire a wealth of biologically me...

A Scalable Framework for Learning the Geometry-Dependent Solution Operators of Partial Differential Equations

Mathematics, Information Science, Computer Science, Artificial Intelligence,
partial differential equations   geometric mapping   neural operator   artificial intelligence   computational efficiency  

Introduction In recent years, solving partial differential equations (PDEs) using numerical methods has played a significant role in various fields such as engineering and medicine. These methods have shown remarkable effectiveness in applications like topology and design optimization as well as clinical prognostication. However, the high computati...

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 ...

Enabling efficient analysis of biobank-scale data with genotype representation graphs

Information Science, Computer Science, Life Sciences, Bioengineering, Genetics,
genotype representation graph   biobank analysis   genetic data compression   whole genome studies   mutation nodes   graph structures   dynamic programming  

Research Based on Genotype Representation Graph (GRG): A New Framework to Enhance Biobank-Scale Data Analysis Efficiency Academic Background and Research Motivation With the rapid advancement of sequencing technologies, the collection of large-scale genomic data has become increasingly common, especially in the field of human disease association st...