Graph embedding on biomedical networks: methods, applications and evaluations

Abstract

Motivation Graph embedding learning that aims to automatically learn low-dimensional node representations, has drawn increasing attention in recent years. To date, most recent graph embedding methods are evaluated on social and information networks and are not comprehensively studied on biomedical networks under systematic experiments and analyses. On the other hand, for a variety of biomedical network analysis tasks, traditional techniques such as matrix factorization (which can be seen as a type of graph embedding methods) have shown promising results, and hence there is a need to systematically evaluate the more recent graph embedding methods (e.g. random walk-based and neural network-based) in terms of their usability and potential to further the state-of-the-art. Results We select 11 representative graph embedding methods and conduct a systematic comparison on 3 important biomedical link prediction tasks: drug-disease association (DDA) prediction, drug–drug interaction (DDI) prediction, protein–protein interaction (PPI) prediction; and 2 node classification tasks: medical term semantic type classification, protein function prediction. Our experimental results demonstrate that the recent graph embedding methods achieve promising results and deserve more attention in the future biomedical graph analysis. Compared with three state-of-the-art methods for DDAs, DDIs and protein function predictions, the recent graph embedding methods achieve competitive performance without using any biological features and the learned embeddings can be treated as complementary representations for the biological features. By summarizing the experimental results, we provide general guidelines for properly selecting graph embedding methods and setting their hyper-parameters for different biomedical tasks. Availability and implementation As part of our contributions in the paper, we develop an easy-to-use Python package with detailed instructions, BioNEV, available at: https://github.com/xiangyue9607/BioNEV, including all source code and datasets, to facilitate studying various graph embedding methods on biomedical tasks. Supplementary information Supplementary data are available at Bioinformatics online.

Knowledge-Based Biomedical Data Science

“…In more basic research applications, broad themes included the use of KGs to produce vector embeddings for prediction or visualization in low dimensional spaces (17,40,48); the use of link prediction methods over KGs to hypothesize previously unobserved relationships (38,40,42,(49)(50)(51)(52)(53)(54)(55)(56)(57)(58); and the use of KGs to generate complex mechanistic accounts of experimental data . Several efforts combined these themes, particularly the use of edge embeddings to improve link prediction (37,50,55,59,60).…”

Drug-Disease Graph: Predicting Adverse Drug Reaction Signals via Graph Neural Network with Clinical Data

“…There have been numerous studies on ADR prediction in pre-marketing phases, attempting graph-based approaches on biomedical information sources [12,15,18,22]. These studies predicted potential side-effects of drug candidate molecules based on their chemical structures [15] and additional biological properties [12].…”

Identifying Small Molecule-miRNA Associations Based on Credible Negative Sample Selection and Random Walk

“…More importantly, some methods, for example, TLHNSMMA (Qu et al, 2018), require numerous computational resources. Inspired by graph embedding methods on biomedical networks (Yue et al, 2020), we developed a new SMiR association prediction model, RWNS, integrating credible negative sample selection, random walk with restart, and diverse biological information into a unified framework. It includes three procedures: similarity computation, negative sample selection, and SMiR association prediction based on random walk with restart on the constructed small molecule-disease-miRNA association network (triple-layer network).…”