A5113156982- Computational Drug Discovery Methods
- Bioinformatics and Genomic Networks
- Metabolomics and Mass Spectrometry Studies
- Early Childhood Education and Development
- Digital literacy in education
- Machine Learning in Bioinformatics
- Child Development and Digital Technology
- Pharmacogenetics and Drug Metabolism
- Global Educational Reforms and Inequalities
- Advanced Graph Neural Networks
- Impact of Technology on Adolescents
Taiyuan University of Technology
2024-2025
Huazhong University of Science and Technology
2023
Personalized cancer drug treatment is emerging as a frontier issue in modern medical research. Considering the genomic differences among patients, determining most effective plan complex and crucial task. In response to these challenges, this study introduces Adaptive Sparse Graph Contrastive Learning Network (ASGCL), an innovative approach unraveling latent interactions context of cell lines drugs. The core ASGCL GraphMorpher module, component that enhances input graph structure via...
Abstract Drug combination therapy is generally more effective than monotherapy in the field of cancer treatment. However, screening for synergistic combinations from a wide range drug particularly important given increase number available classes and potential drug-drug interactions. Existing methods predicting effects primarily focus on extracting structural features molecules cell lines, but neglect interaction mechanisms between lines combinations. Consequently, there deficiency...
Introduction: Synergistic medication, a crucial therapeutic strategy in cancer treatment, involves combining multiple drugs to enhance effectiveness and mitigate side effects. Current research predominantly employs deep learning models for extracting features from cell line drug structure data. However, these methods often overlook the intricate nonlinear relationships within data, neglecting distribution characteristics weighted probability densities of gene expression data...