ArticleInternational journal of molecular sciences2021
iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features.
Article in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- iBitter-HF: A Method for Bitter Peptide Sequence Identification Based on Hybrid Feature Embedding.Foods (Basel, Switzerland) · 2026Article
- Bitter Compounds as Multifunctional Agents against Diabetes-associated Cognitive Dysfunction: Bridging Metabolic Regulation and Neuroprotection.Mini reviews in medicinal chemistry · 2026Review
- BBATProt: a framework predicting biological function with enhanced feature extraction via interpretable deep learning.Briefings in bioinformatics · 2025Article
- xBitterT5: an explainable transformer-based framework with multimodal inputs for identifying bitter-taste peptides.Journal of cheminformatics · 2025Article
- A chemical language model for molecular taste prediction.NPJ science of food · 2025Article
- A systematic review of data and models for predicting food flavor and texture.Current research in food science · 2025Review
- Bitter peptide prediction using graph neural networks.Journal of cheminformatics · 2024Article
- Research on Bitter Peptides in the Field of Bioinformatics: A Comprehensive Review.International journal of molecular sciences · 2024Review
- Predicting multiple taste sensations with a multiobjective machine learning method.NPJ science of food · 2024Article
- An Integrated Comprehensive Peptidomics and In Silico Analysis of Bioactive Peptide-Rich Milk Fermented by Three Autochthonous Cocci Strains.International journal of molecular sciences · 2024Article
- TIPred: a novel stacked ensemble approach for the accelerated discovery of tyrosinase inhibitory peptides.BMC bioinformatics · 2023Article
- Progress of the "Molecular Informatics" Section in 2022.International journal of molecular sciences · 2023Article
- Bioactive and Sensory Di- and Tripeptides Generated during Dry-Curing of Pork Meat.International journal of molecular sciences · 2023Review
- Bitter-RF: A random forest machine model for recognizing bitter peptides.Frontiers in medicine · 2023Article
- Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach.Scientific reports · 2022Article
- IUP-BERT: Identification of Umami Peptides Based on BERT Features.Foods (Basel, Switzerland) · 2022Article
- Identify Bitter Peptides by Using Deep Representation Learning Features.International journal of molecular sciences · 2022Article
- Editorial of Special Issue "Deep Learning and Machine Learning in Bioinformatics".International journal of molecular sciences · 2022Article
- AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning.Scientific reports · 2022Article
- A survey on computational taste predictors.European food research and technology = Zeitschrift fur Lebensmittel-Untersuchung und -Forschung. A · 2022Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Accurate identification of bitter peptides is of great importance for better understanding their biochemical and biophysical properties. To date, machine learning-based methods have become effective approaches for providing a good avenue for identifying potential bitter peptides from large-scale protein datasets. Although few machine learning-based predictors have been developed for identifying the bitterness of peptides, their prediction performances could be improved. In this study, we developed a new predictor (named iBitter-Fuse) for achieving more accurate identification of bitter peptides. In the proposed iBitter-Fuse, we have integrated a variety of feature encoding schemes for providing sufficient information from different aspects, namely consisting of compositional information and physicochemical properties. To enhance the predictive performance, the customized genetic algorithm utilizing self-assessment-report (GA-SAR) was employed for identifying informative features followed by inputting optimal ones into a support vector machine (SVM)-based classifier for developing the final model (iBitter-Fuse). Benchmarking experiments based on both 10-fold cross-validation and independent tests indicated that the iBitter-Fuse was able to achieve more accurate performance as compared to state-of-the-art methods. To facilitate the high-throughput identification of bitter peptides, the iBitter-Fuse web server was established and made freely available online. It is anticipated that the iBitter-Fuse will be a useful tool for aiding the discovery and de novo design of bitter peptides.
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Registered trials
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