ArticleNature communications2023
Hierarchical graph learning for protein-protein interaction.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 papers.
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Who cites it
83 citing papers in PubMed.
- TripleBind: a generalizable deep learning framework for protein-nucleic acid and protein-ligand binding sites prediction based on pre-trained protein language models.Molecular diversity · 2026Article
- DyProL: Dynamic Ensemble Representation Learning for Protein-Nucleic Acid Binding Site Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Decoupling topological and molecular features for interpretable biomolecular interaction prediction.Briefings in bioinformatics · 2026Article
- scMOG: A graph neural network method for regulatory relationship-preserving single-cell multi-omics integration.Quantitative biology (Beijing, China) · 2026Article
- Next-generation kidney tissue analysis - spatial omics and digital pathology.Nature reviews. Nephrology · 2026Review
- Structure-based virulence factor classification using a dual-driven graph transformer with a pretrained language model.Briefings in bioinformatics · 2026Article
- Hierarchical Graph Representation Learning for Protein-Protein Interaction Site Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- DynaTCR: dynamic hard-negative ensemble graph learning improves TCR-epitope binding prediction.Bioinformatics (Oxford, England) · 2026Article
- Hierarchical Contrastive Learning for Protein-Protein Interaction Prediction Across Organisms.International journal of molecular sciences · 2026Article
- Machine Learning-Empowered Electromagnetic Wave Absorbing Materials: From Forward Prediction to Generative Inverse Design.Molecules (Basel, Switzerland) · 2026Review
- Reliable evaluation and learning in multi-input biological association prediction.Briefings in bioinformatics · 2026Article
- Computational prediction of TCR cross-reactivity: principles, challenges and translational opportunities.Journal of translational medicine · 2026Review
- Limitations of the refolding pipeline for de novo protein design.Protein science : a publication of the Protein Society · 2026Article
- Interpretable deep learning framework for mapping E3-substrate binding interfaces.Nature communications · 2026Article
- Deep learning and attention mechanisms to identify key genes and their implications for the origin of insect wings.Scientific reports · 2026Article
- Accessibility in proteins and RNAs interactions prediction with machine learning: are we overlooking non-experts?Briefings in bioinformatics · 2026Review
- Dual-channel graph learning reveals similarity and complementarity in protein-protein interaction networks.PLoS computational biology · 2026Article
- EMGMDA: a multi-modal graph neural framework for robust prediction of miRNA-disease associations.BMC genomics · 2026Article
- LightDTA: lightweight drug-target affinity prediction via random-walk network embedding and knowledge distillation.Molecular diversity · 2026Article
- StabLyzeGraph: High-throughput screening of combinatorial mutations using graph neural networks.Protein science : a publication of the Protein Society · 2026Article
23 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein-Protein Interactions (PPIs) are fundamental means of functions and signalings in biological systems. The massive growth in demand and cost associated with experimental PPI studies calls for computational tools for automated prediction and understanding of PPIs. Despite recent progress, in silico methods remain inadequate in modeling the natural PPI hierarchy. Here we present a double-viewed hierarchical graph learning model, HIGH-PPI, to predict PPIs and extrapolate the molecular details involved. In this model, we create a hierarchical graph, in which a node in the PPI network (top outside-of-protein view) is a protein graph (bottom inside-of-protein view). In the bottom view, a group of chemically relevant descriptors, instead of the protein sequences, are used to better capture the structure-function relationship of the protein. HIGH-PPI examines both outside-of-protein and inside-of-protein of the human interactome to establish a robust machine understanding of PPIs. This model demonstrates high accuracy and robustness in predicting PPIs. Moreover, HIGH-PPI can interpret the modes of action of PPIs by identifying important binding and catalytic sites precisely. Overall, "HIGH-PPI [ https://github.com/zqgao22/HIGH-PPI ]" is a domain-knowledge-driven and interpretable framework for PPI prediction studies.
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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.