ArticleInternational journal of molecular sciences2024
Prediction of Protein-Protein Interactions Based on Integrating Deep Learning and Feature Fusion.
Article in International journal of molecular sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Predicting Protein-Protein Interactions by Convolutional Neural Network Model.Biotech (Basel (Switzerland)) · 2026Article
- Article
- Enhancing the Feature Representation of Protein Sequence Descriptors in Protein-Protein Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2025Article
- Biomolecular Interaction Prediction: The Era of AI.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- The Use and Importance ofArtificial Intelligence in Vaccine Research, Development, and Production.Archives of Razi Institute · 2025Review
- Review
- Prediction of the Trimer Protein Interface Residue Pair by CNN-GRU Model Based on Multi-Feature Map.Nanomaterials (Basel, Switzerland) · 2025Article
- Protein-protein interaction prediction using enhanced features with spaced conjoint triad and amino acid pairwise distance.PeerJ. Computer science · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding protein-protein interactions (PPIs) helps to identify protein functions and develop other important applications such as drug preparation and protein-disease relationship identification. Deep-learning-based approaches are being intensely researched for PPI determination to reduce the cost and time of previous testing methods. In this work, we integrate deep learning with feature fusion, harnessing the strengths of both approaches, handcrafted features, and protein sequence embedding. The accuracies of the proposed model using five-fold cross-validation on Yeast core and Human datasets are 96.34% and 99.30%, respectively. In the task of predicting interactions in important PPI networks, our model correctly predicted all interactions in one-core, Wnt-related, and cancer-specific networks. The experimental results on cross-species datasets, including Caenorhabditis elegans, Helicobacter pylori, Homo sapiens, Mus musculus, and Escherichia coli, also show that our feature fusion method helps increase the generalization capability of the PPI prediction model.
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Registered trials
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