ArticleACS omega2024
MIBPred: Ensemble Learning-Based Metal Ion-Binding Protein Classifier.
Article in ACS omega, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Rethinking bioinformatics in liquid-liquid phase separation: data resources, predictive models, and an event-centric perspective.Briefings in bioinformatics · 2026Review
- Improving B-cell Linear Epitope PredictionCurrent drug targets · 2026Article
- Interpretable prediction of zinc ion location in proteins with ZincSight.Protein science : a publication of the Protein Society · 2025Article
- PCBert-Kla: an efficient prediction method for lysine lactylation sites based on ProtBert and fusion of physicochemical features.Briefings in bioinformatics · 2025Article
- DeepBBMC biology · 2025Article
- Accurate prediction of toxicity peptide and its function using multi-view tensor learning and latent semantic learning framework.Bioinformatics (Oxford, England) · 2025Article
- An artificial intelligence-based approach for identifying the proteins regulating liquid-liquid phase separation.Briefings in bioinformatics · 2025Article
- Review
- Empirical Comparison and Analysis of Artificial Intelligence-Based Methods for Identifying Phosphorylation Sites of SARS-CoV-2 Infection.International journal of molecular sciences · 2024Review
- Research on Bitter Peptides in the Field of Bioinformatics: A Comprehensive Review.International journal of molecular sciences · 2024Review
- iNP_ESM: Neuropeptide Identification Based on Evolutionary Scale Modeling and Unified Representation Embedding Features.International journal of molecular sciences · 2024Article
- Deciphering the Molecular Mechanisms of Polycystic Ovary Syndrome and Flaxseed Therapy Through Transcriptomics and Machine Learning.IET systems biologyArticle
- StackAHTPs: An explainable antihypertensive peptides identifier based on heterogeneous features and stacked learning approach.IET systems biologyArticle
- Improved in Silico Identification of Protein-Protein Interactions Using Deep Learning Approach.IET systems biologyArticle
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Authors and funding
9 authors.
Funding
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Abstract
In biological organisms, metal ion-binding proteins participate in numerous metabolic activities and are closely associated with various diseases. To accurately predict whether a protein binds to metal ions and the type of metal ion-binding protein, this study proposed a classifier named MIBPred. The classifier incorporated advanced Word2Vec technology from the field of natural language processing to extract semantic features of the protein sequence language and combined them with position-specific score matrix (PSSM) features. Furthermore, an ensemble learning model was employed for the metal ion-binding protein classification task. In the model, we independently trained XGBoost, LightGBM, and CatBoost algorithms and integrated the output results through an SVM voting mechanism. This innovative combination has led to a significant breakthrough in the predictive performance of our model. As a result, we achieved accuracies of 95.13% and 85.19%, respectively, in predicting metal ion-binding proteins and their types. Our research not only confirms the effectiveness of Word2Vec technology in extracting semantic information from protein sequences but also highlights the outstanding performance of the MIBPred classifier in the problem of metal ion-binding protein types. This study provides a reliable tool and method for the in-depth exploration of the structure and function of metal ion-binding proteins.
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Registered trials
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