A Literature Review of Recent Graph Embedding Techniques for Biomedical Data

Abstract

With the rapid development of biomedical software and hardware, a large amount of relational data interlinking genes, proteins, chemical components, drugs, diseases, and symptoms has been collected for modern biomedical research. Many graph-based learning methods have been proposed to analyze such type of data, giving a deeper insight into the topology and knowledge behind the biomedical data, which greatly benefit to both academic research and industrial application for human healthcare. However, the main difficulty is how to handle high dimensionality and sparsity of the biomedical graphs. Recently, graph embedding methods provide an effective and efficient way to address the above issues. It converts graph-based data into a low dimensional vector space where the graph structural properties and knowledge information are well preserved. In this survey, we conduct a literature review of recent developments and trends in applying graph embedding methods for biomedical data. We also introduce important applications and tasks in the biomedical domain as well as associated public biomedical datasets.

Deep Structural Knowledge Exploitation and Synergy for Estimating Node Importance Value on Heterogeneous Information Networks

“…Among a few recent attempts, methods with Graph Neural Networks (GNNs) have emerged as a promising direction (Park et al 2019). Due to good knowledge mining ability from high-order topologies, GNNs can produce semantic enrichment to vectorized node represen-tations benefiting downstream tasks (Chen et al 2022a(Chen et al ,b, 2023aYang et al 2023a,b;Zhang et al 2023b;Chen et al 2020). By incorporating specific designs for HINs, GNN-based methods show the potential in dealing with information heterogeneity (Liang et al 2023;Fu and King 2023), especially for node importance estimation.…”

Diagnosis Prediction Using Knowledge Graphs

“…Traditional graph-based analytic methods can falter in certain cases when attempting to process the computational complexity associated with large real-world graphs that are characterised by notable sparsity, high dimensionality, and considerable heterogeneity. Graph embeddings address this issue by converting graph data into a lower dimensional vector space while preserving the structural properties of the graph [23]. Given a graph 𝒢𝒢 = (𝒱𝒱, ℰ) and a predefined dimensionality 𝑑𝑑, where 𝑑𝑑 ≪ |𝒱𝒱|, a graph embedding seeks to transform 𝒢𝒢 into a 𝑑𝑑-dimensional space ℝ 𝑑𝑑 that preserves the information and properties of 𝒢𝒢 as best possible [11].…”

Data Mining of Students’ Consumption Behaviour Pattern Based on Self-Attention Graph Neural Network

“…As a powerful tool for processing non-European structure data, graph neural networks have undergone rapid development and been wide applied, such as the knowledge graph [46], natural language processing [47], graph-based text representation [48] and graph embedding techniques [49]. In particular, some scholars have proposed the graph attention mechanism to improve the performance of node classification [50].…”