ArticleProceedings of the National Academy of Sciences of the United States of America2007
Predicting protein-protein interactions based only on sequences information.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 369 papers.
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Who cites it
369 citing papers in PubMed.
- Artificial intelligence catalyzes antimicrobial peptide design.Synthetic and systems biotechnology · 2027Review
- PMPIHGLL: predicting metabolite-protein interactions using dual hypergraph convolutional networks and large language models.Briefings in bioinformatics · 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
- A robust framework for protein-protein interaction prediction with multi-objective ensemble learning and embedding-based representations.Scientific reports · 2026Article
- Interpretable deep learning framework for mapping E3-substrate binding interfaces.Nature communications · 2026Article
- DSS-PPI: a self-supervised graph learning framework for protein-protein interaction prediction via multimodal sequence semantics.BMC genomics · 2026Article
- A dual-branch graph neural network architecture for drug-target binding affinity prediction.Scientific reports · 2026Article
- Predicting Protein-Protein Interactions by Convolutional Neural Network Model.Biotech (Basel (Switzerland)) · 2026Article
- gPPIpred: A User-Friendly PPI Predictor Based on Protein Molecular Graphs.microPublication biology · 2026Article
- pCPPs-sADNN: predicting cell-penetrating peptides using self-attention based deep neural network.Scientific reports · 2025Article
- Enhancing the Feature Representation of Protein Sequence Descriptors in Protein-Protein Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2025Article
- NADScience advances · 2025Article
- PLM-interact: extending protein language models to predict protein-protein interactions.Nature communications · 2025Article
- DCMF-PPI: a protein-protein interaction predictor based on dynamic condition and multi-feature fusion.BMC bioinformatics · 2025Article
- Advancing virulence factor prediction using protein language models.BMC biology · 2025Article
- HEPAD: enhancing hemolytic peptide prediction with adaptive feature engineering and diverse sequence descriptors.BMC bioinformatics · 2025Article
- A stacked ensemble classifier for the discovery of prokaryotic efflux proteins based on sequence information.BMC genomics · 2025Article
- Sequence-Based Protein-Protein Interaction Prediction and Its Applications in Drug Discovery.Cells · 2025Review
- NPI-HGNN: A Heterogeneous Graph Neural Network-Based Approach for Predicting ncRNA-Protein Interactions.Interdisciplinary sciences, computational life sciences · 2025Article
- Review
309 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Protein-protein interactions (PPIs) are central to most biological processes. Although efforts have been devoted to the development of methodology for predicting PPIs and protein interaction networks, the application of most existing methods is limited because they need information about protein homology or the interaction marks of the protein partners. In the present work, we propose a method for PPI prediction using only the information of protein sequences. This method was developed based on a learning algorithm-support vector machine combined with a kernel function and a conjoint triad feature for describing amino acids. More than 16,000 diverse PPI pairs were used to construct the universal model. The prediction ability of our approach is better than that of other sequence-based PPI prediction methods because it is able to predict PPI networks. Different types of PPI networks have been effectively mapped with our method, suggesting that, even with only sequence information, this method could be applied to the exploration of networks for any newly discovered protein with unknown biological relativity. In addition, such supplementary experimental information can enhance the prediction ability of the method.
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