Sul-BERTGRU: An Ensemble Deep Learning Method Integrating Information Entropy-Enhanced BERT and Directional Multi-GRU for S-Sulfhydration Sites Prediction

Information Science, Artificial Intelligence, Life Sciences, Cell Biology, Biochemistry,
deep learning   S-sulfhydration sites   information entropy   BERT   multi-directional GRU   protein modification   bioinformatics  

Background Introduction Post-Translational Modifications (PTMs) are crucial mechanisms for regulating cellular activities, including gene transcription, DNA repair, and protein interactions. Among these, cysteine, a rare amino acid, participates in various PTMs through its thiol group, playing a significant role in redox balance and signal transduc...

APNet: An Explainable Sparse Deep Learning Model to Discover Differentially Active Drivers of Severe COVID-19

Information Science, Artificial Intelligence, Medicine, Infectious Diseases, Life Sciences, Immunology,
COVID-19   deep learning   bioinformatics   proteomics   network biology  

Academic Background The COVID-19 pandemic has had a significant impact on global public health systems. Although the pandemic has somewhat subsided, its complex immunopathological mechanisms, long-term sequelae (such as “long COVID”), and the potential for similar threats in the future continue to drive in-depth research. Severe COVID-19 cases are ...

Deep Learning to Quantify the Pace of Brain Aging in Relation to Neurocognitive Changes

Information Science, Computer Science, Artificial Intelligence, Medicine, Neurology, Medical Imaging, Life Sciences, Bioengineering,
deep learning   brain aging   neurocognitive changes   Alzheimer's disease   magnetic resonance imaging  

As the global aging problem intensifies, the incidence of neurodegenerative diseases (such as Alzheimer’s Disease, AD) is increasing year by year. Brain aging (Brain Aging, BA) is one of the significant risk factors for neurodegenerative diseases, but it does not completely align with chronological age (Chronological Age, CA). Traditional methods f...

Spectro-Temporal Modulations Incorporated Two-Stream Robust Speech Emotion Recognition

Information Science, Computer Science, Artificial Intelligence,
speech emotion recognition   spectro-temporal modulation features   robustness   deep learning   noisy environment  

Research on Two-Stream Robust Speech Emotion Recognition Based on Spectro-Temporal Modulation Features Academic Background Speech Emotion Recognition (SER) is a technology that identifies emotions by analyzing the emotional content in human speech. It has broad application potential in areas such as human-computer interaction, customer service mana...

Multi-Task Aquatic Toxicity Prediction Model Based on Multi-Level Features Fusion

Chemistry, Computational Chemistry, Environmental Chemistry, Engineering, Environmental Engineering, Information Science, Computer Science, Artificial Intelligence, Medicine, Environmental and Public Health,
aquatic toxicity   deep learning   multi-task model   molecular fingerprints   molecular graph features  

Academic Background With the growing threat of organic compounds to environmental pollution, studying the toxic responses of different aquatic organisms to these compounds has become crucial. Such research not only helps assess the potential ecological impacts of pollutants on the overall aquatic ecosystem but also provides significant scientific f...

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...