ReviewMolecules (Basel, Switzerland)2023
Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review.
Review in Molecules (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
40 citing papers in PubMed.
- AI proteomics: from protein identification to virtual cells.Nature methods · 2026Review
- Graph Neural Networks in Neuroimaging: Current Status and Biostatistical Considerations for Clinical Deployment.Annals of biomedical engineering · 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
- Review
- On the Feasibility of Clinical Studies with Cross-Linking Mass Spectrometry.Journal of proteome research · 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
- Artificial Intelligence in the Design and Development of Nanoparticle Drug Delivery Systems: A Systematic Review.Advances in pharmacological and pharmaceutical sciences · 2026Review
- Capturing protein-protein interactions in plants: recent advances, challenges, and opportunities.Frontiers in molecular biosciences · 2026Review
- Mutagenesis-Centered Integrative Approaches for Identifying Binding Sites in Ion Channels and Uncovering Modulatory Mechanisms.Advances in experimental medicine and biology · 2026Review
- Predicting Protein-Protein Interactions from Machine-Learned Representations.Advances in experimental medicine and biology · 2026Review
- Integrating dual convolutional networks and BiLSTM for precision prediction of chronic myeloid leukemia from protein sequences.Frontiers in genetics · 2026Article
- Enhancing the Feature Representation of Protein Sequence Descriptors in Protein-Protein Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2025Article
- Systematic evaluation of predictors for binding free energy changes upon mutations in protein complexes.Briefings in bioinformatics · 2025Article
- Recent Advances and Application of Machine Learning for Protein-Protein Interaction Prediction in Rice: Challenges and Future Perspectives.Proteomes · 2025Review
- And… cut! - how conformational regulation of CRISPR-Cas effectors directs nuclease activity.The Biochemical journal · 2025Review
- The Use and Importance ofArtificial Intelligence in Vaccine Research, Development, and Production.Archives of Razi Institute · 2025Review
- SageTCR: a structure-based model integrating residue- and atom-level representations for enhanced TCR-pMHC binding prediction.Briefings in bioinformatics · 2025Article
- Bind: large-scale biological interaction network discovery through knowledge graph-driven machine learning.Journal of translational medicine · 2025Article
- RMDNet: RNA-aware dung beetle optimization-based multi-branch integration network for RNA-protein binding sites prediction.BMC bioinformatics · 2025Article
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
Deep learning, a potent branch of artificial intelligence, is steadily leaving its transformative imprint across multiple disciplines. Within computational biology, it is expediting progress in the understanding of Protein-Protein Interactions (PPIs), key components governing a wide array of biological functionalities. Hence, an in-depth exploration of PPIs is crucial for decoding the intricate biological system dynamics and unveiling potential avenues for therapeutic interventions. As the deployment of deep learning techniques in PPI analysis proliferates at an accelerated pace, there exists an immediate demand for an exhaustive review that encapsulates and critically assesses these novel developments. Addressing this requirement, this review offers a detailed analysis of the literature from 2021 to 2023, highlighting the cutting-edge deep learning methodologies harnessed for PPI analysis. Thus, this review stands as a crucial reference for researchers in the discipline, presenting an overview of the recent studies in the field. This consolidation helps elucidate the dynamic paradigm of PPI analysis, the evolution of deep learning techniques, and their interdependent dynamics. This scrutiny is expected to serve as a vital aid for researchers, both well-established and newcomers, assisting them in maneuvering the rapidly shifting terrain of deep learning applications in PPI analysis.
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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.