ArticleBioinformatics (Oxford, England)2018
Predicting protein-protein interactions through sequence-based deep learning.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 142 papers.
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Who cites it
142 citing papers in PubMed.
- TOMOC: a topology-driven multi-objective evolutionary framework for robust overlapping protein complex detection in noisy PPI networks.BMC 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
- Reliable evaluation and learning in multi-input biological association prediction.Briefings in bioinformatics · 2026Article
- A robust framework for protein-protein interaction prediction with multi-objective ensemble learning and embedding-based representations.Scientific reports · 2026Article
- From Prompt to Drug: Toward Pharmaceutical Superintelligence.ACS central science · 2026Review
- An efficient framework for protein-protein interaction prediction by integrating stacked denoising autoencoders and random ferns.iScience · 2026Article
- A comprehensive evaluation of self-attention for detecting regulatory feature interactions.NAR genomics and bioinformatics · 2026Article
- Review
- Quantum-augmented graph differential geometry enhances accuracy in protein-protein interaction prediction.Scientific reports · 2026Article
- Decoding Protein-Membrane Binding Interfaces from Surface-Fingerprint-Based Geometric Deep Learning and Molecular Dynamics Simulations.Journal of chemical information and modeling · 2026Article
- Predicting Protein-Protein Interactions from Machine-Learned Representations.Advances in experimental medicine and biology · 2026Review
- Interactomics: Dissecting Protein Networks in Living Systems.Advances in experimental medicine and biology · 2026Review
- Dual Cross-Attention Network for Hierarchical Feature Fusion in Protein-Protein Interaction Prediction.Computational and structural biotechnology journal · 2026Article
- ProtAttn-QuadNet: An attention-based deep learning framework for protein-protein interaction prediction using ProtBERT embeddings.PloS one · 2026Article
- gPPIpred: A User-Friendly PPI Predictor Based on Protein Molecular Graphs.microPublication biology · 2026Article
- MPIDNN-GPPI: multi-protein language model with an improved deep neural network for generalized protein‒protein interaction prediction.BMC genomics · 2025Article
- Component puzzle protein-protein interaction prediction.Briefings in bioinformatics · 2025Article
- PLM-interact: extending protein language models to predict protein-protein interactions.Nature communications · 2025Article
- Review
- DCMF-PPI: a protein-protein interaction predictor based on dynamic condition and multi-feature fusion.BMC bioinformatics · 2025Article
82 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Motivation: High-throughput experimental techniques have produced a large amount of protein-protein interaction (PPI) data, but their coverage is still low and the PPI data is also very noisy. Computational prediction of PPIs can be used to discover new PPIs and identify errors in the experimental PPI data. Results: We present a novel deep learning framework, DPPI, to model and predict PPIs from sequence information alone. Our model efficiently applies a deep, Siamese-like convolutional neural network combined with random projection and data augmentation to predict PPIs, leveraging existing high-quality experimental PPI data and evolutionary information of a protein pair under prediction. Our experimental results show that DPPI outperforms the state-of-the-art methods on several benchmarks in terms of area under precision-recall curve (auPR), and computationally is more efficient. We also show that DPPI is able to predict homodimeric interactions where other methods fail to work accurately, and the effectiveness of DPPI in specific applications such as predicting cytokine-receptor binding affinities. Availability and implementation: Predicting protein-protein interactions through sequence-based deep learning): https://github.com/hashemifar/DPPI/. Supplementary information: Supplementary data are available at Bioinformatics online.
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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.