ArticleScientific reports2020
SkipGNN: predicting molecular interactions with skip-graph networks.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.
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Who cites it
40 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Systematic Review of the Application of Graph Neural Networks to Extract Candidate Genes and Biological Associations.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · 2025Pooled it
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- Decoupling topological and molecular features for interpretable biomolecular interaction prediction.Briefings in bioinformatics · 2026Article
- IMF-DDI: Information Mapping and Fusion Framework for Drug-drug Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Rule-guided Skip-GCN in neural latent information diffusion network for social recommendation.Scientific reports · 2026Article
- Multi-view knowledge-guided flow subgraphs with substructure initialization for explainable DDI prediction.Briefings in functional genomics · 2026Article
- Integrative multi-omics and network biology in cardiovascular disease: a systems-level framework for translational discovery.Frontiers in systems biology · 2026Review
- Decoding the interactions and functions of non-coding RNA with artificial intelligence.Nature reviews. Molecular cell biology · 2025Review
- RCAN-DDI: Relation-aware cross adversarial network for drug-drug interaction prediction.Journal of pharmaceutical analysis · 2025Article
- PF-AGCN: an adaptive graph convolutional network for protein-protein interaction-based function prediction.Bioinformatics (Oxford, England) · 2025Article
- NASNet-DTI: accurate drug-target interaction prediction using heterogeneous graphs and node adaptation.Briefings in bioinformatics · 2025Article
- Bayesian Neighborhood Adaptation for Graph Neural Networks.Transactions on machine learning research · 2025Article
- Adaptive Multi-Kernel Graph Neural Network for Drug-Drug Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2025Article
- Applications of Machine Learning-Driven Molecular Models for Advancing Ophthalmic Precision Medicine.Bioengineering (Basel, Switzerland) · 2025Review
- Translational Informatics Driven Drug Repositioning for Neurodegenerative Disease.Current neuropharmacology · 2025Review
- Curvature-enhanced graph convolutional network for biomolecular interaction prediction.Computational and structural biotechnology journal · 2024Article
- Node Classification Method Based on Hierarchical Hypergraph Neural Network.Sensors (Basel, Switzerland) · 2024Article
- Accurate and interpretable drug-drug interaction prediction enabled by knowledge subgraph learning.Communications medicine · 2024Article
- Drug-drug interactions prediction based on deep learning and knowledge graph: A review.iScience · 2024Review
- Hierarchical graph contrastive learning of local and global presentation for multimodal sentiment analysis.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular interaction networks are powerful resources for molecular discovery. They are increasingly used with machine learning methods to predict biologically meaningful interactions. While deep learning on graphs has dramatically advanced the prediction prowess, current graph neural network (GNN) methods are mainly optimized for prediction on the basis of direct similarity between interacting nodes. In biological networks, however, similarity between nodes that do not directly interact has proved incredibly useful in the last decade across a variety of interaction networks. Here, we present SkipGNN, a graph neural network approach for the prediction of molecular interactions. SkipGNN predicts molecular interactions by not only aggregating information from direct interactions but also from second-order interactions, which we call skip similarity. In contrast to existing GNNs, SkipGNN receives neural messages from two-hop neighbors as well as immediate neighbors in the interaction network and non-linearly transforms the messages to obtain useful information for prediction. To inject skip similarity into a GNN, we construct a modified version of the original network, called the skip graph. We then develop an iterative fusion scheme that optimizes a GNN using both the skip graph and the original graph. Experiments on four interaction networks, including drug-drug, drug-target, protein-protein, and gene-disease interactions, show that SkipGNN achieves superior and robust performance. Furthermore, we show that unlike popular GNNs, SkipGNN learns biologically meaningful embeddings and performs especially well on noisy, incomplete interaction networks.
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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.