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Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations
- Source :
- Bioinformatics
- Publication Year :
- 2019
- Publisher :
- arXiv, 2019.
-
Abstract
- 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. 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.<br />Comment: Published in Bioinformatics
- Subjects :
- Statistics and Probability
Power graph analysis
FOS: Computer and information sciences
Computer Science - Machine Learning
Graph embedding
Computer science
0206 medical engineering
02 engineering and technology
Machine learning
computer.software_genre
Biochemistry
Machine Learning (cs.LG)
03 medical and health sciences
Text mining
Protein function prediction
Drug Interactions
Molecular Biology
030304 developmental biology
Social and Information Networks (cs.SI)
0303 health sciences
Artificial neural network
business.industry
Proteins
Computer Science - Social and Information Networks
Random walk
Original Papers
Computer Science Applications
Semantics
Computational Mathematics
Computational Theory and Mathematics
Artificial intelligence
Neural Networks, Computer
Data and Text Mining
business
computer
020602 bioinformatics
Software
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Bioinformatics
- Accession number :
- edsair.doi.dedup.....9797d05f4d0f267f5036c815a8f49408
- Full Text :
- https://doi.org/10.48550/arxiv.1906.05017