ArticleComputational and structural biotechnology journal2020
Prediction of human-virus protein-protein interactions through a sequence embedding-based machine learning method.
Article in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers.
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Who cites it
70 citing papers in PubMed.
- Discovery and design of potent cell surface display elements.Nature biotechnology · 2026Article
- Machine Learning and Deep Learning Frameworks for Human-Virus Protein-Protein Interaction Prediction: Emerging Architectures, Methods, Benchmarks, and Challenges.International journal of molecular sciences · 2026Review
- Integrating host-microbiome multi-omics with machine learning: methods, benchmarks, and translational applications.Science China. Life sciences · 2026Review
- Accessibility in proteins and RNAs interactions prediction with machine learning: are we overlooking non-experts?Briefings in bioinformatics · 2026Review
- Spatial transcriptomics uncovers vasculature-centered cellular interactions driving Japanese encephalitis progression in a mouse model.Nature communications · 2026Article
- VHI-Pred: A Multi-Feature-Based Tool for Predicting Human-Virus Protein-Protein Interactions.Molecular biotechnology · 2026Article
- Comprehensive review and assessment of machine learning approaches for host-pathogen protein-protein interaction prediction.Briefings in bioinformatics · 2026Review
- MPIDNN-GPPI: multi-protein language model with an improved deep neural network for generalized protein‒protein interaction prediction.BMC genomics · 2025Article
- Article
- PLM-interact: extending protein language models to predict protein-protein interactions.Nature communications · 2025Article
- Sequence-Based Protein-Protein Interaction Prediction and Its Applications in Drug Discovery.Cells · 2025Review
- Graph neural network integrated with pretrained protein language model for predicting human-virus protein-protein interactions.Briefings in bioinformatics · 2025Article
- Identification of RC3H1 as antiviral host factor binding to the non-structural protein 1 of Influenza A virus via a 3-stage computational pipeline and cell-based analysis.Virology journal · 2025Article
- Unveiling the ghost: machine learning's impact on the landscape of virology.The Journal of general virology · 2025Review
- Nanobody screening and machine learning guided identification of cross-variant anti-SARS-CoV-2 neutralizing heavy-chain only antibodies.PLoS pathogens · 2025Article
- Article
- Computational Analysis of Virus-Host Interactomes.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Bioinformatic Resources for Exploring Human-virus Protein-protein Interactions Based on Binding Modes.Genomics, proteomics & bioinformatics · 2024Review
- HBFormer: a single-stream framework based on hybrid attention mechanism for identification of human-virus protein-protein interactions.Bioinformatics (Oxford, England) · 2024Article
- Prediction of viral oncoproteins through the combination of generative adversarial networks and machine learning techniques.Scientific reports · 2024Article
10 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The identification of human-virus protein-protein interactions (PPIs) is an essential and challenging research topic, potentially providing a mechanistic understanding of viral infection. Given that the experimental determination of human-virus PPIs is time-consuming and labor-intensive, computational methods are playing an important role in providing testable hypotheses, complementing the determination of large-scale interactome between species. In this work, we applied an unsupervised sequence embedding technique (doc2vec) to represent protein sequences as rich feature vectors of low dimensionality. Training a Random Forest (RF) classifier through a training dataset that covers known PPIs between human and all viruses, we obtained excellent predictive accuracy outperforming various combinations of machine learning algorithms and commonly-used sequence encoding schemes. Rigorous comparison with three existing human-virus PPI prediction methods, our proposed computational framework further provided very competitive and promising performance, suggesting that the doc2vec encoding scheme effectively captures context information of protein sequences, pertaining to corresponding protein-protein interactions. Our approach is freely accessible through our web server as part of our host-pathogen PPI prediction platform (http://zzdlab.com/InterSPPI/). Taken together, we hope the current work not only contributes a useful predictor to accelerate the exploration of human-virus PPIs, but also provides some meaningful insights into human-virus relationships.
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