A5004359972- Topic Modeling
- Bioinformatics and Genomic Networks
- Biomedical Text Mining and Ontologies
- Gene expression and cancer classification
- Advanced Graph Neural Networks
- Machine Learning in Bioinformatics
- Domain Adaptation and Few-Shot Learning
- Advanced Text Analysis Techniques
- Probiotics and Fermented Foods
- Pharmacogenetics and Drug Metabolism
- Gut microbiota and health
- Neural Networks and Applications
- Data Quality and Management
- Machine Learning in Materials Science
- Natural Language Processing Techniques
- Bacterial biofilms and quorum sensing
- Computational Drug Discovery Methods
Zhengzhou University
2020-2025
Predicting the associations between human microbes and drugs (MDAs) is one critical step in drug development precision medicine areas. Since discovering these through wet experiments time-consuming labor-intensive, computational methods have already been an effective way to tackle this problem. Recently, graph contrastive learning (GCL) approaches shown great advantages embeddings of nodes from heterogeneous biological graphs (HBGs). However, most GCL-based don't fully capture rich structure...
Abstract Motivation Discovering the drug–target interactions (DTIs) is a crucial step in drug development such as identification of side effects and repositioning. Since identifying DTIs by web-biological experiments time-consuming costly, many computational-based approaches have been proposed become an efficient manner to infer potential interactions. Although extensive effort invested solve this task, prediction accuracy still needs be improved. More especially, heterogeneous network-based...
The measurement of gene functional similarity plays a critical role in numerous biological applications, such as clustering, the construction networks. However, most existing approaches still rely heavily on traditional computational strategies, which are not guaranteed to achieve satisfactory performance. In this study, we propose novel approach called GOGCN measure by modeling Gene Ontology (GO) through Graph Convolutional Network (GCN). is graph-based that performs sufficient...
Recently, with the foundation and development of gene ontology (GO) resources, numerous works have been proposed to compute functional similarity genes achieved series successes in some research fields. Focusing on calculation information content (IC) terms is main idea these methods, which essential for measuring genes. However, most approaches deficiencies, especially when IC both GO their corresponding annotated term sets. To this end, accurately still challenging.In article, we a novel...
In recent years, functional similarity has played an independent role in some biological fields such as gene clustering, prediction, and evaluation for protein-protein interaction. this premise, effective methods have already been proposed based on Gene Ontology (GO). Although these mainstream achieve the purpose measuring similarity, they may deficiency when calculating Information Content (IC) of GO terms. Consequently, accurately is still a meaningful objective research. paper, novel...