ArticleBriefings in bioinformatics2021
LSTM-PHV: prediction of human-virus protein-protein interactions by LSTM with word2vec.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
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Who cites it
56 citing papers in PubMed.
- Article
- 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
- Graph and Hypergraph Theories Applied to Dynamic Protein-Protein Interaction Network Analysis, and Deep-Learning Frameworks for Protein Complex Network Prediction.International journal of molecular sciences · 2026Review
- Accessibility in proteins and RNAs interactions prediction with machine learning: are we overlooking non-experts?Briefings in bioinformatics · 2026Review
- Multilabel prediction of virus target proteins via multimodal graph representation learning.PLoS computational biology · 2026Article
- Comprehensive review and assessment of machine learning approaches for host-pathogen protein-protein interaction prediction.Briefings in bioinformatics · 2026Review
- A Systematic Evaluation of Protein Phase Separation Predictors across Diverse Protein Landscapes.Computational and structural biotechnology journal · 2026Article
- Accurate identification of broadly neutralizing antibodies against dengue virus based on deep stacking strategy with multi-perspective features.Scientific reports · 2025Article
- 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
- Dual-protein embedding-based graph model with dynamic attention for interaction prediction.Briefings in bioinformatics · 2025Article
- Sequence-only prediction of binding affinity changes: a robust and interpretable model for antibody engineering.Bioinformatics (Oxford, England) · 2025Article
- A genetic algorithm-based ensemble model for efficiently identifying interleukin 6 inducing peptides.Scientific reports · 2025Article
- Recent advances in deep learning for protein-protein interaction: a review.BioData mining · 2025Review
- Recent progress and future challenges in structure-based protein-protein interaction prediction.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Review
- A review of machine learning methods for imbalanced data challenges in chemistry.Chemical science · 2025Review
- GTE-PPIS: a protein-protein interaction site predictor based on graph transformer and equivariant graph neural network.Briefings in bioinformatics · 2025Article
- VITALdb: to select the best viroinformatics tools for a desired virus or application.Briefings in bioinformatics · 2025Article
- Predicting the bacterial host range of plasmid genomes using the language model-based one-class support vector machine algorithm.Microbial genomics · 2025Article
- DeepRNAac4C: a hybrid deep learning framework for RNA N4-acetylcytidine site prediction.Frontiers in genetics · 2025Article
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4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Viral infection involves a large number of protein-protein interactions (PPIs) between human and virus. The PPIs range from the initial binding of viral coat proteins to host membrane receptors to the hijacking of host transcription machinery. However, few interspecies PPIs have been identified, because experimental methods including mass spectrometry are time-consuming and expensive, and molecular dynamic simulation is limited only to the proteins whose 3D structures are solved. Sequence-based machine learning methods are expected to overcome these problems. We have first developed the LSTM model with word2vec to predict PPIs between human and virus, named LSTM-PHV, by using amino acid sequences alone. The LSTM-PHV effectively learnt the training data with a highly imbalanced ratio of positive to negative samples and achieved AUCs of 0.976 and 0.973 and accuracies of 0.984 and 0.985 on the training and independent datasets, respectively. In predicting PPIs between human and unknown or new virus, the LSTM-PHV learned greatly outperformed the existing state-of-the-art PPI predictors. Interestingly, learning of only sequence contexts as words is sufficient for PPI prediction. Use of uniform manifold approximation and projection demonstrated that the LSTM-PHV clearly distinguished the positive PPI samples from the negative ones. We presented the LSTM-PHV online web server and support data that are freely available at http://kurata35.bio.kyutech.ac.jp/LSTM-PHV.
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