ArticleScientific reports2025
Artificial intelligence-powered prediction of AIM-2 inflammasome sequences using transformers and graph attention networks in periodontal inflammation.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transformer-based models in dentistry: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Artificial intelligence in periodontal disease research: a bibliometric and visualized analysis of global research trends (2007-2025).The Saudi dental journal · 2026Review
- Artificial Intelligence in Endodontic Education: A Systematic Review with Frequentist and Bayesian Meta-Analysis of Student-Based Evidence.Dentistry journal · 2025Review
- [Applications and perspectives of artificial intelligence in periodontology].Hua xi kou qiang yi xue za zhi = Huaxi kouqiang yixue zazhi = West China journal of stomatology · 2025Review
- Innovative animal models for surgical interventions and implant biocompatibility: a translational perspective.Annals of medicine and surgery (2012) · 2025Review
- Identification and analysis of neutrophil extracellular trap-related genes in periodontitis via bioinformatics and experimental verification.BMC oral health · 2025Article
- Comprehensive single-cell transcriptome analysis of autologous platelet-rich plasma therapy on human thin endometrium.Scientific reports · 2025Article
- Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Periodontal inflammation is a chronic condition affecting the tissues surrounding teeth. Initiated by dental plaque, it triggers an immune response leading to tissue destruction. The AIM-2 inflammasome regulates this response, and understanding its peptide sequences could aid in developing targeted therapeutics. This study explores using transformers and graph attention networks (GAT) to treat periodontal inflammation. UniProt was used to download AIM-2 inflammasome proteins and FASTA sequences with 100%, 90%, and 50% similarity. DeepBio, a web service for developing deep-learning architectures, analyzed these sequences. Peptide sequence prediction methods were evaluated using a transformer, RNN-CNN, and GAT models. The transformer model achieved 84% accuracy, the GAT model 86%, and the RNN-CNN 64%. Both transformer and GAT models predicted peptide sequences more effectively than the RNN-CNN model, with the Transformer showing the highest class accuracy at 85%, followed by the GAT model at 80%. Models exhibited varying sensitivity and specificity, with the Transformer demonstrating superior performance in overall and class-specific peptide sequence prediction. AI-based peptide sequence prediction using transformers, GAT, and RNN-CNN shows promise for accurately predicting AIM-2 peptide sequences, with transformers and GAT outperforming RNN-CNN in accuracy and class accuracy.
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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.