A5077611132- Advanced Graph Neural Networks
- Graph Theory and Algorithms
- Machine Learning in Materials Science
- Computational Drug Discovery Methods
- RNA Research and Splicing
- RNA modifications and cancer
- Machine Learning in Bioinformatics
- RNA and protein synthesis mechanisms
- Anomaly Detection Techniques and Applications
- Bioinformatics and Genomic Networks
- Multimodal Machine Learning Applications
- Glycosylation and Glycoproteins Research
- Topic Modeling
- Epigenetics and DNA Methylation
- Domain Adaptation and Few-Shot Learning
- Mechanical stress and fatigue analysis
- Explainable Artificial Intelligence (XAI)
- Machine Learning in Healthcare
- Network Security and Intrusion Detection
- Recommender Systems and Techniques
- Gear and Bearing Dynamics Analysis
- Artificial Intelligence in Healthcare
- Hand Gesture Recognition Systems
- Complex Network Analysis Techniques
- Biomedical Text Mining and Ontologies
Central South University
2020-2024
Zhejiang University of Technology
2024
Drug–drug interaction (DDI) has attracted widespread attention because when incompatible drugs are taken together, DDI will lead to adverse effects on the body, such as drug poisoning or reduced efficacy. The of closely determined by molecular structures involved. To represent data effectively, researchers usually treat structure a molecule graph. Then, previous studies can use handcrafted graph neural network (GNN) model learn representations for prediction. However, in field...
As the least understood mode of alternative splicing, Intron Retention (IR) is emerging as an interesting area and has attracted more attention in field gene regulation disease studies. Existing methods detect IR exclusively based on one or a few predefined metrics describing local summarized characteristics retained introns. These are not able to describe pattern sequencing depth intronic reads, which intuitive informative characteristic We hypothesize that incorporating distribution reads...
Understanding drug-response differences in cancer treatments is one of the most challenging aspects personalized medicine. Recently, graph neural networks (GNNs) have become state-of-the-art methods many representation learning scenarios bioinformatics. However, building an optimal handcrafted GNN model for a particular drug sensitivity dataset requires manual design and fine-tuning hyperparameters model, which time-consuming expert knowledge.In this work, we propose AutoCDRP, novel...
Intron Retention (IR) is an alternative splicing mode through which introns are retained in mature RNAs rather than being spliced most cases. IR has been gaining increasing attention recent years because of its recognized association with gene expression regulation and complex diseases. Continuous efforts have dedicated to the development detection methods. These methods differ their metrics quantify retention propensity, performance detect events, functional enrichment detected IRs,...
Detecting graph anomalies is a critical research field with applications in various fields, such as fraud detection, health monitoring, cybersecurity, and failure detection. Graph neural networks (GNNs) have become prominent technology anomaly primarily due to their ability extract hidden patterns leverage the inherent properties of data. However, designing fine-tuning GNN architectures time consuming requires expertise. Moreover, most existing GNNs focus solely on spatial information...
Abstract Deep learning has achieved a lot of progress in predicting Remaining Useful Life (RUL). However, contemporary deep frameworks face inherent limitations, including constrained receptive fields, difficulties capturing long-term dependencies, and singularities within the feature extraction domain. In response to these challenges, we propose novel time-frequency enhanced Transformer model for remaining lifespan rolling bearings. this model, utilize causal convolution operations capture...
Differential analysis of gene-level expression is a commonly used approach for identifying disease-associated genes. Recently, intron retention (IR) has been shown to be associated with complex diseases such as cancers. IR provides value that complementary traditional expression. However, systematic method exploit genes remains largely unexplored. We developed pipeline identify the gene based on differential (IRDAG), which integrates events detected by two methods, IRFinder and iREAD....
Glycans play an indispensable role in various bio-logical processes, such as cancer and autoimmune diseases. The function of glycan is closely determined by its structure. Due to the branch nonlinear properties glycans, previous research treats glycans graph structure a topological represent data effectively. Graph neural networks (GNNs) are efficient mining method have many applications bioinformatics. Therefore, researchers successfully used handcrafted GNNs predict immunogenicity....