Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions
arXiv:2306.10456 · doi:10.1055/s-0043-1768735
Abstract
Graph representation learning (GRL) has emerged as a pivotal field that has contributed significantly to breakthroughs in various fields, including biomedicine. The objective of this survey is to review the latest advancements in GRL methods and their applications in the biomedical field. We also highlight key challenges currently faced by GRL and outline potential directions for future research.
arXiv commentAccepted by 2023 IMIA Yearbook of Medical Informatics
References in corpus (18)
- LINE: Large-scale Information Network Embedding
- The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains
- A Survey on Knowledge Graphs: Representation, Acquisition and Applications
- Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
- Complex Embeddings for Simple Link Prediction
- Graph Contrastive Learning with Adaptive Augmentation
- struc2vec: Learning Node Representations from Structural Identity
- MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
- Junction Tree Variational Autoencoder for Molecular Graph Generation
- GraphSAINT: Graph Sampling Based Inductive Learning Method
- Graph Representation Learning in Biomedicine
- GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
- DGCN: Diversified Recommendation with Graph Convolutional Networks
- Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence
- Mining On Alzheimer's Diseases Related Knowledge Graph to Identity Potential AD-related Semantic Triples for Drug Repurposing
- LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs
- Explanations of Black-Box Models based on Directional Feature Interactions
- TuneUp: A Simple Improved Training Strategy for Graph Neural Networks