ArticleInternational journal of molecular sciences2015
An Effective Antifreeze Protein Predictor with Ensemble Classifiers and Comprehensive Sequence Descriptors.
Article in International journal of molecular sciences, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Prediction of enzyme function using an interpretable optimized ensemble learning framework.Chemical science · 2025Article
- Analysis of Ice-Binding Protein Evolution.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Application of Nanoparticles and Melatonin for Cryopreservation of Gametes and Embryos.Current issues in molecular biology · 2022Review
- Cold adaptation strategies in plants-An emerging role of epigenetics and antifreeze proteins to engineer cold resilient plants.Frontiers in genetics · 2022Review
- Prediction and analysis of antifreeze proteins.Heliyon · 2021Article
- Predicting antifreeze proteins with weighted generalized dipeptide composition and multi-regression feature selection ensemble.BMC bioinformatics · 2021Article
- Analysis of the Sequence Characteristics of Antifreeze Protein.Life (Basel, Switzerland) · 2021Article
- ECM-LSE: Prediction of Extracellular Matrix Proteins Using Deep Latent Space Encoding of k-Spaced Amino Acid Pairs.Frontiers in bioengineering and biotechnology · 2021Article
- AFP-LSE: Antifreeze Proteins Prediction Using Latent Space Encoding of Composition of k-Spaced Amino Acid Pairs.Scientific reports · 2020Article
- Identifying Antifreeze Proteins Based on Key Evolutionary Information.Frontiers in bioengineering and biotechnology · 2020Article
- Peptidic Antifreeze Materials: Prospects and Challenges.International journal of molecular sciences · 2019Review
- Protein Sub-Nuclear Localization Based on Effective Fusion Representations and Dimension Reduction Algorithm LDA.International journal of molecular sciences · 2015Article
- JPPRED: Prediction of Types of J-Proteins from Imbalanced Data Using an Ensemble Learning Method.BioMed research international · 2015Article
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4 authors.
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Abstract
Antifreeze proteins (AFPs) play a pivotal role in the antifreeze effect of overwintering organisms. They have a wide range of applications in numerous fields, such as improving the production of crops and the quality of frozen foods. Accurate identification of AFPs may provide important clues to decipher the underlying mechanisms of AFPs in ice-binding and to facilitate the selection of the most appropriate AFPs for several applications. Based on an ensemble learning technique, this study proposes an AFP identification system called AFP-Ensemble. In this system, random forest classifiers are trained by different training subsets and then aggregated into a consensus classifier by majority voting. The resulting predictor yields a sensitivity of 0.892, a specificity of 0.940, an accuracy of 0.938 and a balanced accuracy of 0.916 on an independent dataset, which are far better than the results obtained by previous methods. These results reveal that AFP-Ensemble is an effective and promising predictor for large-scale determination of AFPs. The detailed feature analysis in this study may give useful insights into the molecular mechanisms of AFP-ice interactions and provide guidance for the related experimental validation. A web server has been designed to implement the proposed method.
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