A5080802434- Colorectal Cancer Screening and Detection
- Advanced Neural Network Applications
- Gastrointestinal Bleeding Diagnosis and Treatment
- Industrial Vision Systems and Defect Detection
- vaccines and immunoinformatics approaches
- Glycosylation and Glycoproteins Research
- Misinformation and Its Impacts
- Complex Network Analysis Techniques
- Monoclonal and Polyclonal Antibodies Research
Renmin University of China
2024
Changchun University of Science and Technology
2024
Jiangxi College of Applied Technology
2024
Changchun University
2022
Abstract Accurate prediction of antibody–antigen complex structures is pivotal in drug discovery, vaccine design and disease treatment can facilitate the development more effective therapies diagnostics. In this work, we first review docking (ABAG-docking) datasets. Then, present creation characterization a comprehensive benchmark dataset complexes. We categorize based on difficulty, interface properties structural characteristics, to provide diverse set cases for rigorous evaluation....
Abstract Wireless capsule endoscopy (WCE) is becoming more popular in clinical settings as a safe and painless gastrointestinal examination. Existing studies on automatic detection of lesions WCE images have the problems small dataset size uneven distribution numbers terms categories, which often leads to overfitting model severely limits performance improvement object network images. The traditional data enhancement methods such flipping local erasure limitations cannot achieve good...
Abstract The exponential growth of social network information has a certain degree falsehood, which seriously affects people’s judgment and decision-making even leads to adverse consequences such as unrest. In the research on intelligent detection rumors, existing studies have overlooked coupling relationship between nodes during spread rumors. Therefore, we propose comment graph modeling method for networks, characterizes interaction patterns dissemination mechanisms networks; A hybrid...
Abstract Point cloud target detection completes the interaction of 3D features such as vector position and reflection intensity in coordinate system visualization with visual enhancement effects, which are widely used virtual reality, augmented reality autonomous driving. However, disorder, sparsity overlap LiDAR point clouds increase difficulty recognition. To address this problem, a model named R-PointGNN (Residual-Graph Neural Network) based on graph neural network is proposed. Deep...