Ruiqiang Guo

ORCID: 0009-0005-9445-1121
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About
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Research Areas
  • Service-Oriented Architecture and Web Services
  • ERP Systems Implementation and Impact
  • Information Technology Governance and Strategy
  • Semantic Web and Ontologies
  • Advanced Graph Neural Networks
  • Protein Structure and Dynamics
  • Text and Document Classification Technologies
  • Complex Network Analysis Techniques
  • Web Data Mining and Analysis
  • Computational Drug Discovery Methods
  • Bioinformatics and Genomic Networks

Hebei Normal University
2005-2025

Donghua University
2005

Prediction of protein-ligand interactions is critical for drug discovery and repositioning. Traditional prediction methods are computationally intensive limited in modeling structural changes. In contrast, data-driven deep learning significantly reduce computational costs offer a more efficient approach discovery. However, existing models often fail to fully exploit metadata low-frequency features, leading suboptimal performance on sparse, imbalanced datasets. To address these challenges,...

10.1080/07391102.2025.2475229 article EN Journal of Biomolecular Structure and Dynamics 2025-03-12

10.1109/taslpro.2025.3572352 article EN IEEE Transactions on Audio Speech and Language Processing 2025-01-01

The service discovery based on semantic description plays an important role in the process of Web Service composition. Traditional approaches to modeling similarity between Services compute subsume relationship for function parameters profiles within a single ontology. In this paper, we introduce new graph theoretic framework bipartite matching finding best correspondences among belonging advertisement and request. On computing pair parameters, present novel determining similar entity which...

10.1109/cit.2005.140 article EN 2005-01-01

It has become a tendency to use combination of autoencoders and graph neural networks for attribute clustering solve the community detection problem. However, existing methods do not consider influence differences between node neighborhood information high-order information, fusion structural features is insufficient. In order make better we propose model named fusing attention network (CDFG). Specifically, firstly an autoencoder learn features. Then only calculates weight on target but also...

10.3390/math10214155 article EN cc-by Mathematics 2022-11-07

This paper proposes an information system framework for production collaboration and standardization in Chinese liquor industry, examines the key implementation issues of systems production. A descriptive single case study is conducted to explore identify benefits, challenges, success factors by taking a famous producer as target company. The shows that proposed helpful improve Some benefits challenges implementing are different from those described previous studies because various distinct,...

10.4018/ijeis.2016010103 article EN International Journal of Enterprise Information Systems 2016-01-01
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