ArticleACS omega2022
Improved Prediction Model of Protein and Peptide Toxicity by Integrating Channel Attention into a Convolutional Neural Network and Gated Recurrent Units.
Article in ACS omega, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 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.
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Who cites it
35 citing papers in PubMed, 82 citations in OpenAlex.
- ToxiCompass: an evidence-tracked benchmark for short-peptide toxicity screening and confidence-aware assay prioritization.Briefings in bioinformatics · 2026Article
- UniPept: A Universal Model for Predicting Peptide Properties Based on Atom-Residue Dual-Scale Integration.Interdisciplinary sciences, computational life sciences · 2026Article
- Contemporary data-driven innovations in peptide-based therapeutic design.Briefings in bioinformatics · 2026Review
- A Review of the Chemical Properties, Mechanisms of Action, and Structure-Activity Relationships of Jellyfish Toxins.Current issues in molecular biology · 2026Review
- BiGvCL: bipartite graph-based cross-domain contrastive learning model for the predicting drug-gene interactions.Briefings in bioinformatics · 2026Article
- 3D spine reconstruction from a single radiograph based on GANs.Medical & biological engineering & computing · 2026Article
- Advancing toxicity AI-based prediction with multilevel systems biology: a case study on genotoxicity.Briefings in bioinformatics · 2025Article
- MFE-ACVP: anti-coronavirus peptide prediction based on multimodal feature extraction and ensemble learning.Briefings in bioinformatics · 2025Article
- DeepPlantAllergy: deep learning for explainable prediction of allergenicity in plant proteins.Briefings in bioinformatics · 2025Article
- EGFR Mutation Detection in Whole Slide Images of Non-Small Cell Lung Cancers Using a Two-Stage Deep Transfer Learning Approach.Cancer medicine · 2025Article
- BioWalk-MDA: a novel approach for large-scale predicting metabolite-drug associations based on multi layered biomedical knowledge graphs.Briefings in bioinformatics · 2025Article
- MVRBind: multi-view learning for RNA-small molecule binding site prediction.Briefings in bioinformatics · 2025Article
- Accurate structure prediction of cyclic peptides containing unnatural amino acids using HighFold3.Briefings in bioinformatics · 2025Article
- Advancing ADMET prediction through multiscale fragment-aware pretraining with MSformer-ADMET.Briefings in bioinformatics · 2025Article
- Enhancing cross-domain protein and peptide interaction with retrained deep learning models.Briefings in bioinformatics · 2025Article
- SeekTP: identifying multifunctional therapeutic peptides via graph attention and deep property integration.Briefings in bioinformatics · 2025Article
- Integrating ESM‑2 and Graph Neural Networks with AlphaFold‑2 Structures for Enhanced Protein Function Prediction.ACS omega · 2025Article
- Exo-Tox: Identifying Exotoxins from secreted bacterial proteins.BioData mining · 2025Article
- Kinase-inhibitor binding affinity prediction with pretrained graph encoder and language model.Briefings in bioinformatics · 2025Article
- Graph-RPI: predicting RNA-protein interactions via graph autoencoder and self-supervised learning strategies.Briefings in bioinformatics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent times, the importance of peptides in the biomedical domain has received increasing concern in terms of their effect on multiple disease treatments. However, before successful large-scale implementation in the industry, accurate identification of peptide toxicity is a vital prerequisite. The existing computational methods have reached good results from toxicity prediction, and we present an improved model based on different deep learning architectures. The modification mainly focuses on two aspects: sequence encoding and variational information bottlenecks. Consequently, one of our modified plans shows an obvious increase in sensitivity, while the rest show good performance meanwhile adding novelty in the peptide toxicity prediction domain. In detail, our best model could achieve an accuracy of 97.38 and 95.03% in protein and peptide toxicity predictions, respectively. The performance was superior to previous predictors on the same datasets.
Identifiers
What OpenQuestion holds
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.