A5101551804- Advanced Graph Neural Networks
- Natural Language Processing Techniques
- Topic Modeling
- Protein Tyrosine Phosphatases
- Cytokine Signaling Pathways and Interactions
- Complex Network Analysis Techniques
- Graph Theory and Algorithms
- Protein Structure and Dynamics
- Adenosine and Purinergic Signaling
- Machine Learning in Bioinformatics
- Bioinformatics and Genomic Networks
Hong Kong University of Science and Technology
2023-2024
University of Hong Kong
2023-2024
Large language models (LLMs) have achieved impressive success across various domains, but their capability in understanding and resolving complex graph problems is less explored. To bridge this gap, we introduce GraphInstruct, a novel instruction-tuning dataset aimed at enabling to tackle broad spectrum of through explicit reasoning paths. Utilizing build GraphWiz, an open-source model capable solving computational while generating clear processes. further enhance the model's performance...
Data imputation is a crucial task due to the widespread occurrence of missing data. Many methods adopt two-step approach: initially crafting preliminary (the "draft") and then refining it produce final data result, commonly referred as "draft-then-refine". In our study, we examine this prevalent strategy through lens graph Dirichlet energy. We observe that basic "draft" tends decrease Therefore, subsequent "refine" step necessary restore overall energy balance. Existing refinement...
Proteins govern a wide range of biological systems. Evaluating the changes in protein properties upon mutation is fundamental application design, where modeling 3D structure principal task for AI-driven computational approaches. Existing deep learning (DL) approaches represent as geometric graph and simplify to different degrees, thereby failing capture low-level atom patterns high-level amino acid simultaneously. In addition, limited training samples with ground truth labels structures...