A5040914816- Computational Drug Discovery Methods
- Machine Learning in Materials Science
- Protein Structure and Dynamics
- Infrared Target Detection Methodologies
- Advanced Image Fusion Techniques
- Metaheuristic Optimization Algorithms Research
- Remote-Sensing Image Classification
- Face and Expression Recognition
- Neural Networks and Applications
- Fault Detection and Control Systems
- Advanced Semiconductor Detectors and Materials
- Advanced Measurement and Detection Methods
- CCD and CMOS Imaging Sensors
- Evolutionary Algorithms and Applications
- Genetics, Bioinformatics, and Biomedical Research
- Statistical and Computational Modeling
- Bioinformatics and Genomic Networks
- Advanced Multi-Objective Optimization Algorithms
China Pharmaceutical University
2024-2025
Chongqing University of Technology
2024
University of Aizu
2002-2003
Evolutionary programming (EP) has been applied with success to many numerical and combinatorial optimization problems in recent years. However, most analytical experimental results on EP have obtained using low-dimensional problems. It is interesting know whether the empirical from still hold for high-dimensional cases. was discovered that neither classical (CEP) nor fast (FEP) performed satisfactorily some large-scale The paper shows empirically FEP cooperative coevolution (FEPCC) can speed...
Abstract Background : Effective molecular feature representation is crucial for drug property prediction. Recent years have seen increased attention on graph neural networks (GNNs) that are pre‐trained using self‐supervised learning techniques, aiming to overcome the scarcity of labeled data in Traditional GNNs prediction typically perform a single masking operation nodes and edges input graph, only local information insufficient thorough training. Method Hence, we propose model based...
This paper presents an experimental comparison on different kinds of neural network ensemble learning methods a patter classification problems. To summarize, there are three ways designing ensembles in these methods: independent training, sequential training and simultaneous training. The purpose such is not only to illustrate the behavior methods, but also cast light how design more effective ensembles. results show that decision boundary trained by negative correlation almost as good...
ABSTRACT Identifying interactions between drugs and targets is crucial for drug discovery development. Nevertheless, the determination of drug‐target binding affinities (DTAs) through traditional experimental methods a time‐consuming process. Conventional approaches to predicting (DTIs) frequently prove inadequate due an insufficient representation targets, resulting in ineffective feature capture questionable interpretability results. To address these challenges, we introduce CGPDTA, novel...