ArticleBioinformatics (Oxford, England)2023
AFTGAN: prediction of multi-type PPI based on attention free transformer and graph attention network.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- 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
- The role of protein embeddings for protein-protein interaction prediction with graph neural networks.Briefings in bioinformatics · 2026Article
- Dual-channel graph learning reveals similarity and complementarity in protein-protein interaction networks.PLoS computational biology · 2026Article
- Predicting Protein-Protein Interactions from Machine-Learned Representations.Advances in experimental medicine and biology · 2026Review
- MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models.BMC biology · 2025Article
- Prediction of protein-protein interaction based on interaction-specific learning and hierarchical information.BMC biology · 2025Article
- Recent advances in deep learning for protein-protein interaction: a review.BioData mining · 2025Review
- Graph Neural Network-Based Approaches for Protein Function Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Challenges in predicting protein-protein interactions of understudied viruses: Arenavirus-human interactions.Computational and structural biotechnology journal · 2025Article
- Anti-symmetric framework for balanced learning of protein-protein interactions.Bioinformatics (Oxford, England) · 2024Article
- DGCPPISP: a PPI site prediction model based on dynamic graph convolutional network and two-stage transfer learning.BMC bioinformatics · 2024Article
- GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs.BMC genomics · 2024Article
- Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges.Molecules (Basel, Switzerland) · 2024Review
- POOE: predicting oomycete effectors based on a pre-trained large protein language model.mSystems · 2024Article
- Prediction of protein interactions between pine and pine wood nematode using deep learning and multi-dimensional feature fusion.Frontiers in plant science · 2024Article
- Pre-trained protein language model sheds new light on the prediction of Arabidopsis protein-protein interactions.Plant methods · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
motivationProtein-protein interaction (PPI) networks and transcriptional regulatory networks are critical in regulating cells and their signaling. A thorough understanding of PPIs can provide more insights into cellular physiology at normal and disease states. Although numerous methods have been proposed to predict PPIs, it is still challenging for interaction prediction between unknown proteins. In this study, a novel neural network named AFTGAN was constructed to predict multi-type PPIs. Regarding feature input, ESM-1b embedding containing much biological information for proteins was added as a protein sequence feature besides amino acid co-occurrence similarity and one-hot coding. An ensemble network was also constructed based on a transformer encoder containing an AFT module (performing the weight operation on vital protein sequence feature information) and graph attention network (extracting the relational features of protein pairs) for the part of the network framework.
resultsThe experimental results showed that the Micro-F1 of the AFTGAN based on three partitioning schemes (BFS, DFS and the random mode) on the SHS27K and SHS148K datasets was 0.685, 0.711 and 0.867, as well as 0.745, 0.819 and 0.920, respectively, all higher than that of other popular methods. In addition, the experimental comparisons confirmed the performance superiority of the proposed model for predicting PPIs of unknown proteins on the STRING dataset. AVAILABILITY AND IMPLEMENTATION: The source code is publicly available at https://github.com/1075793472/AFTGAN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.