ArticleFrontiers in medicine2023
Bitter-RF: A random forest machine model for recognizing bitter peptides.
Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- iBitter-HF: A Method for Bitter Peptide Sequence Identification Based on Hybrid Feature Embedding.Foods (Basel, Switzerland) · 2026Article
- De novo design and experimental characterization of bitter peptides.NPJ science of food · 2026Article
- Prediction of Digestible and Metabolizable Energy in Swine Feed Using Machine Learning.ACS omega · 2026Article
- A unified multi-scale deep learning framework for molecular property prediction that bridges molecular structures and fingerprinting.Communications chemistry · 2026Article
- Elucidating the Material Basis and Receptor Mechanism of Bitterness inFoods (Basel, Switzerland) · 2026Article
- Copper-Iron Cell Death Axis: Mechanistic Crosstalk, Disease Implications and an Integrated Metallo-Redox-Metabolic Framework.International journal of biological sciences · 2026Review
- xBitterT5: an explainable transformer-based framework with multimodal inputs for identifying bitter-taste peptides.Journal of cheminformatics · 2025Article
- Exploring the Molecular Space of Bitter Peptides via Sensory, Receptor, and Sequence Data.Journal of agricultural and food chemistry · 2025Article
- Fingerprint-enhanced hierarchical molecular graph neural networks for property prediction.Journal of pharmaceutical analysis · 2025Article
- NeuroScale: evolutional scale-based protein language models enable prediction of neuropeptides.BMC biology · 2025Article
- Bitter taste receptors.Chemical senses · 2025Review
- Application of machine learning in the discovery of antimicrobial peptides: exploring their potential for ulcerative colitis therapy.eGastroenterology · 2025Article
- Predicting viral proteins that evade the innate immune system: a machine learning-based immunoinformatics tool.BMC bioinformatics · 2024Article
- Bitter peptide prediction using graph neural networks.Journal of cheminformatics · 2024Article
- MLAFP-XN: Leveraging neural network model for development of antifungal peptide identification tool.Heliyon · 2024Article
- Research on Bitter Peptides in the Field of Bioinformatics: A Comprehensive Review.International journal of molecular sciences · 2024Review
- AMP-RNNpro: a two-stage approach for identification of antimicrobials using probabilistic features.Scientific reports · 2024Article
- BitterMasS: Predicting Bitterness from Mass Spectra.Journal of agricultural and food chemistry · 2024Article
- Applications of single‑cell omics and spatial transcriptomics technologies in gastric cancer (Review).Oncology letters · 2024Review
- SAGESDA: Multi-GraphSAGE networks for predicting SnoRNA-disease associations.Current research in structural biology · 2024Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Bitter peptides are short peptides with potential medical applications. The huge potential behind its bitter taste remains to be tapped. To better explore the value of bitter peptides in practice, we need a more effective classification method for identifying bitter peptides. Methods: In this study, we developed a Random forest (RF)-based model, called Bitter-RF, using sequence information of the bitter peptide. Bitter-RF covers more comprehensive and extensive information by integrating 10 features extracted from the bitter peptides and achieves better results than the latest generation model on independent validation set. Results: The proposed model can improve the accurate classification of bitter peptides (AUROC = 0.98 on independent set test) and enrich the practical application of RF method in protein classification tasks which has not been used to build a prediction model for bitter peptides. Discussion: We hope the Bitter-RF could provide more conveniences to scholars for bitter peptide research.
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