ReviewFrontiers in genetics2019
To Embed or Not: Network Embedding as a Paradigm in Computational Biology.
Review in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers.
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Who cites it
72 citing papers in PubMed.
- Enhancing link prediction in biomedical knowledge graphs with BioPathNet.Nature biomedical engineering · 2026Article
- A multimodal human protein embeddings database: DeepDrug Protein Embeddings Bank (DPEB).NAR genomics and bioinformatics · 2026Article
- Engineered Living Systems With Self-Organizing Neural Networks: From Anatomy to Behavior and Gene Expression.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Combining graphlets and random walks for capturing complex network topology.Scientific reports · 2026Article
- Design and evaluation of semantically-valid negative samples integration techniques for scalable semi-automated drug repurposing prediction pipelines in rare disease research.BMC bioinformatics · 2026Article
- Article
- MDGraphEmb: a toolkit for graph embedding and classification of protein conformational ensembles.Bioinformatics (Oxford, England) · 2025Article
- Image2Reg: Linking chromatin images to gene regulation using genetic and chemical perturbation screens.Cell systems · 2025Article
- Bias-aware training and evaluation of link prediction algorithms in network biology.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Universal multilayer network embedding reveals a causal link between GABA neurotransmitter and cancer.BMC bioinformatics · 2025Article
- Basal Xenobot transcriptomics reveals changes and novel control modality in cells freed from organismal influence.Communications biology · 2025Article
- Uncovering latent biological function associations through gene set embeddings.BMC bioinformatics · 2025Article
- KG2ML: integrating knowledge graphs and positive unlabeled learning for identifying disease-associated genes.Frontiers in bioinformatics · 2025Article
- Narratives from GPT-derived networks of news and a link to financial markets dislocations.International journal of data science and analytics · 2025Article
- Simplicity within biological complexity.Bioinformatics advances · 2025Article
- Elucidating simulated equivalence responding through dynamic visualization of structural connectivity and relational density.Frontiers in artificial intelligence · 2025Article
- Deep representation learning of protein-protein interaction networks for enhanced pattern discovery.Science advances · 2024Article
- Heterogeneous network approaches to protein pathway prediction.Computational and structural biotechnology journal · 2024Review
- Inter-chromosomal contacts demarcate genome topology along a spatial gradient.Nature communications · 2024Article
- Knowledge-slanted random forest method for high-dimensional data and small sample size with a feature selection application for gene expression data.BioData mining · 2024Article
12 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Current technology is producing high throughput biomedical data at an ever-growing rate. A common approach to interpreting such data is through network-based analyses. Since biological networks are notoriously complex and hard to decipher, a growing body of work applies graph embedding techniques to simplify, visualize, and facilitate the analysis of the resulting networks. In this review, we survey traditional and new approaches for graph embedding and compare their application to fundamental problems in network biology with using the networks directly. We consider a broad variety of applications including protein network alignment, community detection, and protein function prediction. We find that in all of these domains both types of approaches are of value and their performance depends on the evaluation measures being used and the goal of the project. In particular, network embedding methods outshine direct methods according to some of those measures and are, thus, an essential tool in bioinformatics research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.